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closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/databricks/hooks/databricks.py", "airflow/providers/databricks/operators/databricks.py", "tests/providers/databricks/hooks/test_databricks.py", "tests/providers/databricks/operators/test_databricks.py"]

Databricks: support for triggering jobs by name

### Description The DatabricksRunNowOperator supports triggering job runs by job ID. We would like to extend the operator to also support triggering jobs by name. This will likely require first making an API call to list jobs in order to find the appropriate job id. ### Use case/motivation _No response_ ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/21380

https://github.com/apache/airflow/pull/21663

537c24433014d3d991713202df9c907e0f114d5d

a1845c68f9a04e61dd99ccc0a23d17a277babf57

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/operators/glue.py"]

Status of testing Providers that were prepared on February 05, 2022

### Body I have a kind request for all the contributors to the latest provider packages release. Could you please help us to test the RC versions of the providers? Let us know in the comment, whether the issue is addressed. Those are providers that require testing as there were some substantial changes introduced: ## Provider [amazon: 3.0.0rc1](https://pypi.org/project/apache-airflow-providers-amazon/3.0.0rc1) - [ ] [Rename params to cloudformation_parameter in CloudFormation operators. (#20989)](https://github.com/apache/airflow/pull/20989): @potiuk - [ ] [[SQSSensor] Add opt-in to disable auto-delete messages (#21159)](https://github.com/apache/airflow/pull/21159): @LaPetiteSouris - [x] [Create a generic operator SqlToS3Operator and deprecate the MySqlToS3Operator. (#20807)](https://github.com/apache/airflow/pull/20807): @mariotaddeucci - [ ] [Move some base_aws logging from info to debug level (#20858)](https://github.com/apache/airflow/pull/20858): @o-nikolas - [ ] [Adds support for optional kwargs in the EKS Operators (#20819)](https://github.com/apache/airflow/pull/20819): @ferruzzi - [ ] [AwsAthenaOperator: do not generate client_request_token if not provided (#20854)](https://github.com/apache/airflow/pull/20854): @XD-DENG - [ ] [Add more SQL template fields renderers (#21237)](https://github.com/apache/airflow/pull/21237): @josh-fell - [ ] [fix: cloudwatch logs fetch logic (#20814)](https://github.com/apache/airflow/pull/20814): @ayushchauhan0811 - [ ] [Alleviate import warning for `EmrClusterLink` in deprecated AWS module (#21195)](https://github.com/apache/airflow/pull/21195): @josh-fell - [ ] [Rename amazon EMR hook name (#20767)](https://github.com/apache/airflow/pull/20767): @vinitpayal - [ ] [Standardize AWS SQS classes names (#20732)](https://github.com/apache/airflow/pull/20732): @eladkal - [ ] [Standardize AWS Batch naming (#20369)](https://github.com/apache/airflow/pull/20369): @ferruzzi - [ ] [Standardize AWS Redshift naming (#20374)](https://github.com/apache/airflow/pull/20374): @ferruzzi - [ ] [Standardize DynamoDB naming (#20360)](https://github.com/apache/airflow/pull/20360): @ferruzzi - [ ] [Standardize AWS ECS naming (#20332)](https://github.com/apache/airflow/pull/20332): @ferruzzi - [ ] [Refactor operator links to not create ad hoc TaskInstances (#21285)](https://github.com/apache/airflow/pull/21285): @josh-fell ## Provider [apache.druid: 2.3.0rc1](https://pypi.org/project/apache-airflow-providers-apache-druid/2.3.0rc1) - [ ] [Add more SQL template fields renderers (#21237)](https://github.com/apache/airflow/pull/21237): @josh-fell ## Provider [apache.hive: 2.2.0rc1](https://pypi.org/project/apache-airflow-providers-apache-hive/2.2.0rc1) - [ ] [Add more SQL template fields renderers (#21237)](https://github.com/apache/airflow/pull/21237): @josh-fell ## Provider [apache.spark: 2.1.0rc1](https://pypi.org/project/apache-airflow-providers-apache-spark/2.1.0rc1) - [ ] [Add more SQL template fields renderers (#21237)](https://github.com/apache/airflow/pull/21237): @josh-fell ## Provider [apache.sqoop: 2.1.0rc1](https://pypi.org/project/apache-airflow-providers-apache-sqoop/2.1.0rc1) - [ ] [Add more SQL template fields renderers (#21237)](https://github.com/apache/airflow/pull/21237): @josh-fell ## Provider [cncf.kubernetes: 3.0.2rc1](https://pypi.org/project/apache-airflow-providers-cncf-kubernetes/3.0.2rc1) - [ ] [Add missed deprecations for cncf (#20031)](https://github.com/apache/airflow/pull/20031): @dimon222 ## Provider [docker: 2.4.1rc1](https://pypi.org/project/apache-airflow-providers-docker/2.4.1rc1) - [ ] [Fixes Docker xcom functionality (#21175)](https://github.com/apache/airflow/pull/21175): @ferruzzi ## Provider [exasol: 2.1.0rc1](https://pypi.org/project/apache-airflow-providers-exasol/2.1.0rc1) - [ ] [Add more SQL template fields renderers (#21237)](https://github.com/apache/airflow/pull/21237): @josh-fell ## Provider [google: 6.4.0rc1](https://pypi.org/project/apache-airflow-providers-google/6.4.0rc1) - [ ] [[Part 1]: Add hook for integrating with Google Calendar (#20542)](https://github.com/apache/airflow/pull/20542): @rsg17 - [ ] [Add encoding parameter to `GCSToLocalFilesystemOperator` to fix #20901 (#20919)](https://github.com/apache/airflow/pull/20919): @danneaves-ee - [ ] [batch as templated field in DataprocCreateBatchOperator (#20905)](https://github.com/apache/airflow/pull/20905): @wsmolak - [ ] [Make timeout Optional for wait_for_operation (#20981)](https://github.com/apache/airflow/pull/20981): @MaksYermak - [ ] [Add more SQL template fields renderers (#21237)](https://github.com/apache/airflow/pull/21237): @josh-fell - [ ] [Cloudsql import links fix. (#21199)](https://github.com/apache/airflow/pull/21199): @subkanthi - [ ] [Refactor operator links to not create ad hoc TaskInstances (#21285)](https://github.com/apache/airflow/pull/21285): @josh-fell ## Provider [http: 2.0.3rc1](https://pypi.org/project/apache-airflow-providers-http/2.0.3rc1) - [ ] [Split out confusing path combination logic to separate method (#21247)](https://github.com/apache/airflow/pull/21247): @malthe ## Provider [imap: 2.2.0rc1](https://pypi.org/project/apache-airflow-providers-imap/2.2.0rc1) - [ ] [Add "use_ssl" option to IMAP connection (#20441)](https://github.com/apache/airflow/pull/20441): @feluelle ## Provider [jdbc: 2.1.0rc1](https://pypi.org/project/apache-airflow-providers-jdbc/2.1.0rc1) - [ ] [Add more SQL template fields renderers (#21237)](https://github.com/apache/airflow/pull/21237): @josh-fell ## Provider [microsoft.azure: 3.6.0rc1](https://pypi.org/project/apache-airflow-providers-microsoft-azure/3.6.0rc1) - [ ] [Refactor operator links to not create ad hoc TaskInstances (#21285)](https://github.com/apache/airflow/pull/21285): @josh-fell ## Provider [microsoft.mssql: 2.1.0rc1](https://pypi.org/project/apache-airflow-providers-microsoft-mssql/2.1.0rc1) - [ ] [Add more SQL template fields renderers (#21237)](https://github.com/apache/airflow/pull/21237): @josh-fell ## Provider [microsoft.psrp: 1.1.0rc1](https://pypi.org/project/apache-airflow-providers-microsoft-psrp/1.1.0rc1) - [x] [PSRP improvements (#19806)](https://github.com/apache/airflow/pull/19806): @malthe ## Provider [mysql: 2.2.0rc1](https://pypi.org/project/apache-airflow-providers-mysql/2.2.0rc1) - [ ] [Add more SQL template fields renderers (#21237)](https://github.com/apache/airflow/pull/21237): @josh-fell (https://github.com/apache/airflow/pull/20618): @potiuk ## Provider [oracle: 2.2.0rc1](https://pypi.org/project/apache-airflow-providers-oracle/2.2.0rc1) - [x] [Add more SQL template fields renderers (#21237)](https://github.com/apache/airflow/pull/21237): @josh-fell - [x] [Fix handling of Oracle bindvars in stored procedure call when parameters are not provided (#20720)](https://github.com/apache/airflow/pull/20720): @malthe ## Provider [postgres: 3.0.0rc1](https://pypi.org/project/apache-airflow-providers-postgres/3.0.0rc1) - [ ] [Replaces the usage of postgres:// with postgresql:// (#21205)](https://github.com/apache/airflow/pull/21205): @potiuk - [ ] [Add more SQL template fields renderers (#21237)](https://github.com/apache/airflow/pull/21237): @josh-fell - [ ] [Remove `:type` lines now sphinx-autoapi supports typehints (#20951)](https://github.com/apache/airflow/pull/20951): @ashb - [ ] [19489 - Pass client_encoding for postgres connections (#19827)](https://github.com/apache/airflow/pull/19827): @subkanthi - [ ] [Amazon provider remove deprecation, second try (#19815)](https://github.com/apache/airflow/pull/19815): @uranusjr ## Provider [qubole: 2.1.0rc1](https://pypi.org/project/apache-airflow-providers-qubole/2.1.0rc1) - [x] [Add Qubole how to documentation (#20058)](https://github.com/apache/airflow/pull/20058): @kazanzhy ## Provider [slack: 4.2.0rc1](https://pypi.org/project/apache-airflow-providers-slack/4.2.0rc1) - [ ] [Return slack api call response in slack_hook (#21107)](https://github.com/apache/airflow/pull/21107): @pingzh (https://github.com/apache/airflow/pull/20571): @potiuk ## Provider [snowflake: 2.5.0rc1](https://pypi.org/project/apache-airflow-providers-snowflake/2.5.0rc1) - [ ] [Add more SQL template fields renderers (#21237)](https://github.com/apache/airflow/pull/21237): @josh-fell - [ ] [Fix #21096: Support boolean in extra__snowflake__insecure_mode (#21155)](https://github.com/apache/airflow/pull/21155): @mik-laj ## Provider [sqlite: 2.1.0rc1](https://pypi.org/project/apache-airflow-providers-sqlite/2.1.0rc1) - [ ] [Add more SQL template fields renderers (#21237)](https://github.com/apache/airflow/pull/21237): @josh-fell ## Provider [ssh: 2.4.0rc1](https://pypi.org/project/apache-airflow-providers-ssh/2.4.0rc1) - [ ] [Add a retry with wait interval for SSH operator (#14489)](https://github.com/apache/airflow/issues/14489): @Gaurang033 - [ ] [Add banner_timeout feature to SSH Hook/Operator (#21262)](https://github.com/apache/airflow/pull/21262): @potiuk ## Provider [tableau: 2.1.4rc1](https://pypi.org/project/apache-airflow-providers-tableau/2.1.4rc1) - [ ] [Squelch more deprecation warnings (#21003)](https://github.com/apache/airflow/pull/21003): @uranusjr ## Provider [vertica: 2.1.0rc1](https://pypi.org/project/apache-airflow-providers-vertica/2.1.0rc1) - [ ] [Add more SQL template fields renderers (#21237)](https://github.com/apache/airflow/pull/21237): @josh-fell ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/21348

https://github.com/apache/airflow/pull/21353

8da7af2bc0f27e6d926071439900ddb27f3ae6c1

d1150182cb1f699e9877fc543322f3160ca80780

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/templates/airflow/trigger.html", "airflow/www/views.py", "tests/www/views/test_views_trigger_dag.py"]

Override the dag run_id from within the ui

### Description It would be great to have the ability to override the generated run_ids like `scheduled__2022-01-27T14:00:00+00:00` so that it is easier to find specific dag runs in the ui. I know the rest api allows you to specify a run id, but it would be great if ui users could also specify a run_id using for example dag_run conf. ### Use case/motivation _No response_ ### Related issues _No response_ ### Are you willing to submit a PR? - [x] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/21336

https://github.com/apache/airflow/pull/21851

340180423a687d8171413c0c305f2060f9722177

14a2d9d0078569988671116473b43f86aba1161b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/cncf/kubernetes/hooks/kubernetes.py", "airflow/providers/cncf/kubernetes/operators/spark_kubernetes.py", "tests/providers/apache/flink/operators/test_flink_kubernetes.py", "tests/providers/cncf/kubernetes/hooks/test_kubernetes_pod.py", "tests/providers/cncf/kubernetes/operators/test_spark_kubernetes.py"]

on_kill method for SparkKubernetesOperator

### Description In some cases the Airflow sends `SIGTERM` to the task, here to the `SparkKubernetesOperator`, it needs to send `SIGTERM` also to the corresponding pods/jobs ### Use case/motivation _No response_ ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/21325

https://github.com/apache/airflow/pull/29977

feab21362e2fee309990a89aea39031d94c5f5bd

9a4f6748521c9c3b66d96598036be08fd94ccf89

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/example_dags/example_ecs_ec2.py", "airflow/providers/amazon/aws/example_dags/example_ecs_fargate.py", "airflow/providers/amazon/aws/operators/ecs.py", "docs/apache-airflow-providers-amazon/operators/ecs.rst", "tests/providers/amazon/aws/operators/test_ecs.py"]

ECS Operator does not support launch type "EXTERNAL"

### Description You can run ECS tasks either on EC2 instances or via AWS Fargate, and these will run in AWS. With ECS Anywhere, you are now able to run the same ECS tasks on any host that has the ECS agent - on prem, in another cloud provider, etc. The control plane resides in ECS, but the execution of the task is managed by the ECS agent. To launch tasks on hosts that are managed by ECS Anywhere, you need to specify a launch type of EXTERNAL. This is currently not supported by the ECS Operator. When you attempt do this, you get an error of unsupported launch type. The current work around is to use boto3 and create a task and then run it using the correct parameters. ### Use case/motivation The ability to run your task code to support hybrid and multi-cloud orchestration scenarios. ### Related issues _No response_ ### Are you willing to submit a PR? - [x] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/21321

https://github.com/apache/airflow/pull/22093

33ecca1b9ab99d9d15006df77757825c81c24f84

e63f6e36d14a8cd2462e80f26fb4809ab8698380

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/package.json", "airflow/www/static/js/graph.js", "airflow/www/static/js/tree/Tree.jsx", "airflow/www/static/js/tree/dagRuns/index.test.jsx", "airflow/www/static/js/tree/index.jsx", "airflow/www/static/js/tree/renderTaskRows.jsx", "airflow/www/static/js/tree/renderTaskRows.test.jsx", "airflow/www/static/js/tree/useTreeData.js", "airflow/www/static/js/tree/useTreeData.test.jsx", "airflow/www/yarn.lock"]

Pause auto-refresh when the document becomes hidden

### Description When running Airflow it can be common to leave some tabs of Airflow open but not active. I believe (but not 100% sure, if I am wrong I can close this issue) Airflow's auto-refresh keeps refreshing when the document becomes hidden (for example, you switched to another browser tab). This is not desirable in the cases when you are running the Airflow services on your same machine and you have a long-running DAG (taking hours to run). This could cause your CPU utilization to ramp up in this scenario (which can be quite common for users, myself included): 1. You are running the Airflow services on your same machine 2. Your machine is not that powerful 3. You have a long-running DAG (taking hours to run) 4. You leave a auto-refreshing page(s) of that DAG open for a long time (such as tree or graph) in hidden (or non-focused) tabs of your browser - What can make this even worse is if you have multiple tabs like this open, you are multiplying the extra processing power to refresh the page at a short interval 5. You have not increased the default `auto_refresh_interval` of 3 ### Use case/motivation I am proposing the following improvements to the auto-refresh method to improve this situation: 1. When you change tabs in your browser, there is a feature of Javascript in modern browsers called "Page Visibility API". It allows for the use of listeners on a `visibilitychange` event to know when a document becomes visible or hidden. This can be used to pause auto-refresh when the document becomes hidden. - Discussion on Stack Overflow: https://stackoverflow.com/questions/1060008/is-there-a-way-to-detect-if-a-browser-window-is-not-currently-active - MDN: https://developer.mozilla.org/en-US/docs/Web/API/Page_Visibility_API - W3C: https://www.w3.org/TR/page-visibility/ 2. We should provide a message in the UI to alert the user that the auto-refreshing is paused until the page regains focus. 3. Lastely, the option to only auto-refresh if the document is visible should be a configurable setting. Additionally, the older `onblur` and `onfocus` listeners on the entire document could be used too. That way if a user switches to a different window while the page is still visible, the auto-refresh can pause (although this might not be desirable if you want to have Airflow open side-by-side with something else, so maybe this will be overboard) ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/21302

https://github.com/apache/airflow/pull/21904

dfd9805a23b2d366f5c332f4cb4131462c5ba82e

635fe533700f284da9aa04a38a5dae9ad6485454

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/gantt.js", "airflow/www/static/js/graph.js", "airflow/www/static/js/task_instances.js", "airflow/www/views.py"]

Add Trigger Rule Display to Graph View

### Description This feature would introduce some visual addition(s) (e.g. tooltip) to the Graph View to display the trigger rule between tasks. ### Use case/motivation This would add more detail to Graph View, providing more information visually about the relationships between upstream and downstream tasks. ### Related issues https://github.com/apache/airflow/issues/19939 ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/21201

https://github.com/apache/airflow/pull/26043

bdc3d4da3e0fb11661cede149f2768acb2080d25

f94176bc7b28b496c34974b6e2a21781a9afa221

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/connection_form.js"]

"Test Connection" functionality in the UI doesn't consider custom form fields

### Apache Airflow version main (development) ### What happened When creating a connection to Snowflake in the Airflow UI, I was hoping to test the connection prior to saving the connection. Unfortunately when I click on the Test button I receive the following error despite all fields in the connection form (especially Account) being provided.  However, when I specify the connection parameters directly in the Extra field, the connection test is successful.  ### What you expected to happen I would have expected that I could use the custom fields in the connection form to test the connection. While using the Extra field is a workaround for the Snowflake connection type, not all custom connection forms expose the Extra field (e.g. Azure Data Factory, Salesforce, etc.) which makes this workaround impossible when testing in the UI. ### How to reproduce - Attempt to create a Snowflake connection (or other connection types that have the `test_connection()` method implemented which use custom fields for authentication). - Click the Test button in the UI. ### Operating System Debian GNU/Linux 10 (buster ### Versions of Apache Airflow Providers N/A - using `main` branch. ### Deployment Other ### Deployment details Testing with `main` using Breeze. ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/21188

https://github.com/apache/airflow/pull/21330

fc44836504129664edb81c510e6deb41a7e1126d

a9b8ac5e0dde1f1793687a035245fde73bd146d4

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/sensors/external_task.py", "tests/sensors/test_external_task_sensor.py"]

Webserver "External DAG" button on ExternalTaskSensor not working when dag_id is templated

### Apache Airflow version 2.0.2 ### What happened When an ExternalTaskSensor receives a templated dag_id , the web interface's "External DAG" button does not resolve the template, so the destination URL does not work (although the sensor correctly waits for the dag_id that the template refers to)    ### What you expected to happen The button's destination URL should point to the templated dag_id. ### How to reproduce 1. Create a DAG with an ExternalTaskSensor whose dag_id is templated ```python @provide_session def get_last_run_id(execution_date: datetime, session: Any, **context: Any) -> datetime: dag_id = context['task_instance'].xcom_pull('get_idemp', key='id_emp') while (last_run := get_last_dagrun(dag_id, session, include_externally_triggered=True)) is None: continue return last_run.execution_date sensor = ExternalTaskSensor( task_id="wait_for_dag", external_dag_id="{{ ti.xcom_pull('get_idemp', key='id_emp') }}", external_task_id="update_load_registry", <---------- Last DAG operator execution_date_fn=get_last_run_id ) ``` 2. Trigger the created DAG 3. Click on the ExternalTaskSensor operator 4. Click on the "External DAG" button ### Operating System Debian GNU/Linux 10 (buster) (on KubernetesExecutor) ### Versions of Apache Airflow Providers _No response_ ### Deployment Other 3rd-party Helm chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/21183

https://github.com/apache/airflow/pull/21192

6b88d432d959df73433528fe3d62194239f13edd

8da7af2bc0f27e6d926071439900ddb27f3ae6c1

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/cncf/kubernetes/operators/kubernetes_pod.py", "airflow/providers/cncf/kubernetes/utils/pod_manager.py", "kubernetes_tests/test_kubernetes_pod_operator.py", "tests/providers/cncf/kubernetes/operators/test_kubernetes_pod.py"]

`is_delete_operator_pod=True` and `random_name_suffix=False` can cause KubernetesPodOperator to delete the wrong pod

### Apache Airflow version 2.2.2 ### What happened When running multiple KubernetesPodOperators with `random_name_suffix=False` and `is_delete_pod_operator=True` the following will happen: 1) The first task will create the Pod `my-pod` 2) The second task will attempt to create the pod, but fail with a 409 response from the API server (this is expected) 3) The second task will delete `my-pod`, because it has `is_delete_pod_operator=True` and the Pod name is consistent between the two tasks. This is unexpected and will cause the first task to fail as well. I understand that this is a rare circumstance, but I think its still worth fixing as anyone using `random_name_suffix=False` in an otherwise default KubernetesPodOperator may result in other pods being killed. As a possible fix, we could [`find_pod`](https://github.com/apache/airflow/blob/684fe46158aa3d6cb2de245d29e20e487d8f2158/airflow/providers/cncf/kubernetes/operators/kubernetes_pod.py#L322) before deleting, to ensure that the pod being deleted has the appropriate `execution_date` label: https://github.com/apache/airflow/blob/ad07923606262ef8a650dcead38183da6bbb5d7b/airflow/providers/cncf/kubernetes/utils/pod_launcher.py#L103-L112 Let me know if you have any other suggestions for how this could be fixed, or if this should just be considered expected behaviour when using fixed Kubernetes Pod IDs. ### What you expected to happen The second task should be able to fail without deleting the pod from the first task. ### How to reproduce Create a DAG with a single KubernetesPodOperator with `random_name_suffix=False` and `is_delete_pod_operator=True` and run it twice in parallel. ### Operating System Debian GNU/Linux 10 (buster) ### Versions of Apache Airflow Providers apache-airflow-providers-cncf-kubernetes=2.2.0 ### Deployment Other 3rd-party Helm chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/21169

https://github.com/apache/airflow/pull/22092

864cbc9cd843db94ca8bab2187b50de714fdb070

78ac48872bd02d1c08c6e55525f0bb4d6e983d32

python

closed

apache/airflow

https://github.com/apache/airflow

[".pre-commit-config.yaml", ".rat-excludes", "dev/breeze/autocomplete/Breeze2-complete-bash.sh", "dev/breeze/autocomplete/Breeze2-complete-fish.sh", "dev/breeze/autocomplete/Breeze2-complete-zsh.sh", "dev/breeze/setup.cfg", "dev/breeze/src/airflow_breeze/breeze.py", "dev/breeze/src/airflow_breeze/utils/run_utils.py"]

Breeze2 autocomplete requires `click-complete` to be installed

### Apache Airflow version main (development) ### What happened When I setup autocomplete for Breeze2 on a "bare" system when I have no packages installed, It fails autocomplete with this error: ``` ModuleNotFoundError: No module named 'click_completion' Traceback (most recent call last): File "/home/jarek/.pyenv/versions/3.7.9/bin/Breeze2", line 33, in <module> sys.exit(load_entry_point('apache-airflow-breeze', 'console_scripts', 'Breeze2')()) File "/home/jarek/.pyenv/versions/3.7.9/bin/Breeze2", line 25, in importlib_load_entry_point return next(matches).load() File "/home/jarek/.local/lib/python3.7/site-packages/importlib_metadata/__init__.py", line 194, in load module = import_module(match.group('module')) File "/home/jarek/.pyenv/versions/3.7.9/lib/python3.7/importlib/__init__.py", line 127, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "<frozen importlib._bootstrap>", line 1006, in _gcd_import File "<frozen importlib._bootstrap>", line 983, in _find_and_load File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 677, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 728, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/home/jarek/code/airflow/dev/breeze/src/airflow_breeze/breeze.py", line 24, in <module> import click_completion ModuleNotFoundError: No module named 'click_completion' ``` It seems that "autocomplete" feature of Breeze2 requires `click-completion` to be installed first. This is a small issue an small prerequisite but I think it is not handled by the currrent `setup-autocomplete` The same happens if you install Breeze2 with `pipx`. ### What you expected to happen I expect that `click-completion` package is automatically installled in when `./Breeze2 setup-autocomplete` is executed. Also it should be described in the documentation as prerequisite ### How to reproduce * make sure you have no packages installed in your Python "system environment" (for example: `pip list | xargs pip uninstall -y` ) * type ./Breeze2<TAB> ### Operating System Linux Mint 20.1.3 ### Versions of Apache Airflow Providers Not relevant ### Deployment Other ### Deployment details No airflow deployment ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/21164

https://github.com/apache/airflow/pull/22695

c758c76ac336c054fd17d4b878378aa893b7a979

4fb929a60966e9cecbbb435efd375adcc4fff9d7

python

closed

apache/airflow

https://github.com/apache/airflow

["dev/breeze/src/airflow_breeze/breeze.py"]

`setup-autocomplete` in Breeze2 fails with "Permission denied"

### Apache Airflow version main (development) ### What happened When I run "setup-autocomplete" in the new Breeze with zsh it fails with "permission denied" as it tries to access `/.zshrc`: ``` [jarek:~/code/airflow] static-check-breeze2+ ± ./Breeze2 setup-autocomplete Installing zsh completion for local user Activation command scripts are created in this autocompletion path: /home/jarek/code/airflow/.build/autocomplete/Breeze2-complete.zsh Do you want to add the above autocompletion scripts to your zsh profile? [y/N]: y This will modify the /.zshrc file Traceback (most recent call last): File "/home/jarek/code/airflow/.build/breeze2/venv/bin/Breeze2", line 33, in <module> sys.exit(load_entry_point('apache-airflow-breeze', 'console_scripts', 'Breeze2')()) File "/home/jarek/code/airflow/.build/breeze2/venv/lib/python3.8/site-packages/click/core.py", line 1128, in __call__ return self.main(*args, **kwargs) File "/home/jarek/code/airflow/.build/breeze2/venv/lib/python3.8/site-packages/click/core.py", line 1053, in main rv = self.invoke(ctx) File "/home/jarek/code/airflow/.build/breeze2/venv/lib/python3.8/site-packages/click/core.py", line 1659, in invoke return _process_result(sub_ctx.command.invoke(sub_ctx)) File "/home/jarek/code/airflow/.build/breeze2/venv/lib/python3.8/site-packages/click/core.py", line 1395, in invoke return ctx.invoke(self.callback, **ctx.params) File "/home/jarek/code/airflow/.build/breeze2/venv/lib/python3.8/site-packages/click/core.py", line 754, in invoke return __callback(*args, **kwargs) File "/home/jarek/code/airflow/dev/breeze/src/airflow_breeze/breeze.py", line 248, in setup_autocomplete write_to_shell(command_to_execute, script_path, breeze_comment) File "/home/jarek/code/airflow/dev/breeze/src/airflow_breeze/breeze.py", line 215, in write_to_shell with open(script_path, 'a') as script_file: PermissionError: [Errno 13] Permission denied: '/.zshrc' [jarek:~/code/airflow] static-check-breeze2+ 6s 1 ± ``` ### What you expected to happen I expected the scripts in my `${HOME}` directory to be updated with auto-complete but apparently it tries to update a file in `root' folder. ### How to reproduce * Have zsh as your shell * Run `./Breeze2 setup-autocomplete` ### Operating System Linux Mint 20.1.3 ### Versions of Apache Airflow Providers - Not relevant - ### Deployment Other ### Deployment details No airflow - this is just a development environment. ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/21163

https://github.com/apache/airflow/pull/21636

ea21cb3320afcb8150f463ebc8d01798a5d34166

42469f0434f1377c9f9dfd78cbf033ef21e2e505

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/trino/hooks/trino.py", "tests/providers/trino/hooks/test_trino.py"]

apache-airflow-providers-trino issue when I use "run" method, query is not performed

### Apache Airflow version 2.2.3 (latest released) ### What happened When I use Trino provider I use "run" method in TrinoHook, because I need run "create table" query. But these queries are not performed on Trino server. ### What you expected to happen I expected, that queries that were started from "run" method should be performed) ### How to reproduce Create DAG file with Trino provider with this code and some CREATE Table hql request. ``` ... th = TrinoHook() @task(task_id="create_table") def create_table(ds=None, **kwargs): th.run("CREATE TABLE IF NOT EXISTS ...", handler=next_page) cpt = create_table() cpt ``` This code does not perform operation, but right POST request is sent to trino server. ### Operating System Kubuntu, 20.04.3 LTS ### Versions of Apache Airflow Providers apache-airflow-providers-trino=2.0.1 ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else I think it is needed to add support of handler for run method in provider hook in file airflow/providers/trino/hooks/trino.py In this case we get possibility to write handler, that can perform fetchAll() on returned Cursor object, for example Something like this: ``` def run( self, hql, autocommit: bool = False, parameters: Optional[dict] = None, handler=None ) -> None: """Execute the statement against Trino. Can be used to create views.""" return super().run(sql=self._strip_sql(hql), parameters=parameters, handler=handler) ``` Because now code does not send handler to dbapi parent method ` return super().run(sql=self._strip_sql(hql), parameters=parameters)` ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/21125

https://github.com/apache/airflow/pull/21479

dc03000de80e672de661c84f5fbb916413211550

1884f2227d1e41d7bb37246ece4da5d871036c1f

python

closed

apache/airflow

https://github.com/apache/airflow

["BREEZE.rst", "CONTRIBUTING.rst", "CONTRIBUTORS_QUICK_START.rst", "dev/provider_packages/README.md", "docs/apache-airflow/installation/installing-from-pypi.rst", "scripts/tools/initialize_virtualenv.py"]

Breeze: Setting up local virtualenv

There shoudl be a comand that allows to set-up local virtualenv easly. This involves: * checking is airlfow is installed in "${HOME}/airflow" and warning and suggesting to move elsewhere if so (this is very bad because ${HOME}/airflow is by default where airflow stores all files (logs/config etc). * cleaning the "${HOME}/airflow" and regenerating all necessary folders and files * checking if vitualenv is activated - if not writing helpful message * checking if additional dependencies are installed, and based on the OS, suggest what shoudl be installed (note we do not have Windows support here). * installing aiflow with the right extra ([devel-all] I think ) * initializing the sqlite databases of airflow - both "normal" and "unit test" database

https://github.com/apache/airflow/issues/21105

https://github.com/apache/airflow/pull/22971

03f7d857e940b9c719975e72ded4a89f183b0100

03bef084b3f1611e1becdd6ad0ff4c0d2dd909ac

python

closed

apache/airflow

https://github.com/apache/airflow

["BREEZE.rst", "dev/breeze/src/airflow_breeze/commands/developer_commands.py", "dev/breeze/src/airflow_breeze/shell/enter_shell.py", "images/breeze/output-commands.svg", "images/breeze/output-exec.svg"]

Breeze: Exec'ing into running Breeze

`./Breeze2 exec` should exec into the currently running Breeze (or fail with helpful message if Breeze is not running).

https://github.com/apache/airflow/issues/21104

https://github.com/apache/airflow/pull/23052

b6db0e90aeb30133086716a433cab9dca7408a54

94c3203e86252ed120d624a70aed571b57083ea4

python

closed

apache/airflow

https://github.com/apache/airflow

["dev/breeze/src/airflow_breeze/breeze.py", "dev/breeze/src/airflow_breeze/ci/build_image.py", "dev/breeze/src/airflow_breeze/ci/build_params.py", "dev/breeze/src/airflow_breeze/prod/prod_params.py"]

Breeze: Add 'prepare-image-cache' in Breeze

We have a separate command that prepares image caches. It is very similar to "building image" but: * it should prepare both prod and CI images * the `docker build` command should be slighly modified (--cache-to and some other commands) * validation on whether the `buildx plugin` needs to be performed (and command should fail if not with helpful message) Those "differences" between standard build-image can be found with `PREPARE_BUILDX_CACHE` variable == "true" usage in old breeze

https://github.com/apache/airflow/issues/21102

https://github.com/apache/airflow/pull/22344

4e4c0574cdd3689d22e2e7d03521cb82179e0909

dc75f5d8768c8a42df29c86beb519de282539e1f

python

closed

apache/airflow

https://github.com/apache/airflow

["dev/breeze/src/airflow_breeze/breeze.py", "dev/breeze/src/airflow_breeze/ci/build_params.py", "dev/breeze/src/airflow_breeze/global_constants.py", "dev/breeze/src/airflow_breeze/prod/__init__.py", "dev/breeze/src/airflow_breeze/prod/build_prod_image.py", "dev/breeze/src/airflow_breeze/prod/prod_params.py", "dev/breeze/src/airflow_breeze/utils/docker_command_utils.py", "dev/breeze/src/airflow_breeze/utils/path_utils.py", "dev/breeze/src/airflow_breeze/utils/run_utils.py", "dev/breeze/tests/test_prod_image.py"]

Breeze: Build PROD image with Breeze

Similarly to building CI image, we should build PROD image

https://github.com/apache/airflow/issues/21100

https://github.com/apache/airflow/pull/21956

7418720ce173ca5d0c5f5197c168e43258af8cc3

4eebabb76d1d50936c4b63669a93358f4d100ce3

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/snowflake/hooks/snowflake.py", "tests/providers/snowflake/hooks/test_snowflake.py"]

Snowflake connection, boolean value of extra__snowflake__insecure_mode interpreted as string

### Apache Airflow version 2.2.3 (latest released) ### What happened Error thrown when using SnowflakeOperator with a Snowflake Connection. After creating a Snowflake Connection, the "Extra" field was automatically filled with a dictionary containing the values entered in the other input fields. Note: the value for the key "extra__snowflake__insecure_mode" is a boolean.  A task using SnowflakeOperator fails, throwing following error: ``` [2022-01-25, 14:39:54 UTC] {taskinstance.py:1700} ERROR - Task failed with exception Traceback (most recent call last): File "/home/test_airflow2/.local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1329, in _run_raw_task self._execute_task_with_callbacks(context) File "/home/test_airflow2/.local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1455, in _execute_task_with_callbacks result = self._execute_task(context, self.task) File "/home/test_airflow2/.local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1511, in _execute_task result = execute_callable(context=context) File "/home/test_airflow2/.local/lib/python3.8/site-packages/airflow/providers/snowflake/operators/snowflake.py", line 129, in execute execution_info = hook.run(self.sql, autocommit=self.autocommit, parameters=self.parameters) File "/home/test_airflow2/.local/lib/python3.8/site-packages/airflow/providers/snowflake/hooks/snowflake.py", line 293, in run with closing(self.get_conn()) as conn: File "/home/test_airflow2/.local/lib/python3.8/site-packages/airflow/providers/snowflake/hooks/snowflake.py", line 236, in get_conn conn_config = self._get_conn_params() File "/home/test_airflow2/.local/lib/python3.8/site-packages/airflow/providers/snowflake/hooks/snowflake.py", line 170, in _get_conn_params insecure_mode = to_boolean( File "/home/test_airflow2/.local/lib/python3.8/site-packages/airflow/utils/strings.py", line 30, in to_boolean return False if astring is None else astring.lower() in ['true', 't', 'y', 'yes', '1'] AttributeError: 'bool' object has no attribute 'lower' [2022-01-25, 14:39:54 UTC] {taskinstance.py:1267} INFO - Marking task as FAILED. dag_id=test, task_id=snowflake_task, execution_date=20220123T000000, start_date=20220125T133954, end_date=20220125T133954 [2022-01-25, 14:39:55 UTC] {standard_task_runner.py:89} ERROR - Failed to execute job 4 for task snowflake_task Traceback (most recent call last): File "/home/test_airflow2/.local/lib/python3.8/site-packages/airflow/task/task_runner/standard_task_runner.py", line 85, in _start_by_fork args.func(args, dag=self.dag) File "/home/test_airflow2/.local/lib/python3.8/site-packages/airflow/cli/cli_parser.py", line 48, in command return func(*args, **kwargs) File "/home/test_airflow2/.local/lib/python3.8/site-packages/airflow/utils/cli.py", line 92, in wrapper return f(*args, **kwargs) File "/home/test_airflow2/.local/lib/python3.8/site-packages/airflow/cli/commands/task_command.py", line 298, in task_run _run_task_by_selected_method(args, dag, ti) File "/home/test_airflow2/.local/lib/python3.8/site-packages/airflow/cli/commands/task_command.py", line 107, in _run_task_by_selected_method _run_raw_task(args, ti) File "/home/test_airflow2/.local/lib/python3.8/site-packages/airflow/cli/commands/task_command.py", line 180, in _run_raw_task ti._run_raw_task( File "/home/test_airflow2/.local/lib/python3.8/site-packages/airflow/utils/session.py", line 70, in wrapper return func(*args, session=session, **kwargs) File "/home/test_airflow2/.local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1329, in _run_raw_task self._execute_task_with_callbacks(context) File "/home/test_airflow2/.local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1455, in _execute_task_with_callbacks result = self._execute_task(context, self.task) File "/home/test_airflow2/.local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1511, in _execute_task result = execute_callable(context=context) File "/home/test_airflow2/.local/lib/python3.8/site-packages/airflow/providers/snowflake/operators/snowflake.py", line 129, in execute execution_info = hook.run(self.sql, autocommit=self.autocommit, parameters=self.parameters) File "/home/test_airflow2/.local/lib/python3.8/site-packages/airflow/providers/snowflake/hooks/snowflake.py", line 293, in run with closing(self.get_conn()) as conn: File "/home/test_airflow2/.local/lib/python3.8/site-packages/airflow/providers/snowflake/hooks/snowflake.py", line 236, in get_conn conn_config = self._get_conn_params() File "/home/test_airflow2/.local/lib/python3.8/site-packages/airflow/providers/snowflake/hooks/snowflake.py", line 170, in _get_conn_params insecure_mode = to_boolean( File "/home/test_airflow2/.local/lib/python3.8/site-packages/airflow/utils/strings.py", line 30, in to_boolean return False if astring is None else astring.lower() in ['true', 't', 'y', 'yes', '1'] AttributeError: 'bool' object has no attribute 'lower' [2022-01-25, 14:39:55 UTC] {local_task_job.py:154} INFO - Task exited with return code 1 [2022-01-25, 14:39:55 UTC] {local_task_job.py:264} INFO - 0 downstream tasks scheduled from follow-on schedule check ``` This error seems to arise because the boolean value of extra__snowflake__insecure_mode is interpreted as a string. Manually modifying the boolean extra__snowflake__insecure_mode value to be string in the "Extra" dictionary solves this problem: **false->"false"**  ### What you expected to happen Be able to create a usable Snowflake Connection, by filling in fields other than "Extra". The "Extra" field should be automatically filled with a correct/usable connection dictionary. I can then use this Snowflake Connection for SnowflakeOperators in DAGs. ### How to reproduce Create a new Connection of type **Snowflake**, set an arbitrary Connection ID. The rest of the fields can be left empty (doesn't affect error).  Create a DAG with a SnowflakeOperator task, which uses the created Snowflake Connection: ``` from airflow import DAG from airflow.providers.snowflake.operators.snowflake import SnowflakeOperator from airflow.utils.dates import days_ago with DAG('test', start_date=days_ago(2)) as dag: snowflake_task = SnowflakeOperator(task_id='snowflake_task', sql='select 1;', snowflake_conn_id='snowflake_conn') ``` Execute the DAG, the task will fail and throw the above error. ### Operating System Ubuntu 20.04.2 LTS ### Versions of Apache Airflow Providers apache-airflow-providers-snowflake==2.4.0 ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/21096

https://github.com/apache/airflow/pull/21155

14cfefa44c0fd2bcbd0290fc5b4f7b6e3d0cf2d9

534e9ae117641b4147542f2deec2a077f0a42e2f

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/executors/kubernetes_executor.py", "tests/executors/test_kubernetes_executor.py"]

KubernetesJobWatcher failing on HTTP 410 errors, jobs stuck in scheduled state

### Apache Airflow version 2.2.3 (latest released) ### What happened After upgrading Airflow to 2.2.3 (from 2.2.2) and cncf.kubernetes provider to 3.0.1 (from 2.0.3) we started to see these errors in the logs: ``` {"asctime": "2022-01-25 08:19:39", "levelname": "ERROR", "process": 565811, "name": "airflow.executors.kubernetes_executor.KubernetesJobWatcher", "funcName": "run", "lineno": 111, "message": "Unknown error in KubernetesJobWatcher. Failing", "exc_info": "Traceback (most recent call last):\n File \"/usr/local/lib/python3.9/site-packages/airflow/executors/kubernetes_executor.py\", line 102, in run\n self.resource_version = self._run(\n File \"/usr/local/lib/python3.9/site-packages/airflow/executors/kubernetes_executor.py\", line 145, in _run\n for event in list_worker_pods():\n File \"/usr/local/lib/python3.9/site-packages/kubernetes/watch/watch.py\", line 182, in stream\n raise client.rest.ApiException(\nkubernetes.client.exceptions.ApiException: (410)\nReason: Expired: too old resource version: 655595751 (655818065)\n"} Process KubernetesJobWatcher-6571: Traceback (most recent call last): File "/usr/local/lib/python3.9/multiprocessing/process.py", line 315, in _bootstrap self.run() File "/usr/local/lib/python3.9/site-packages/airflow/executors/kubernetes_executor.py", line 102, in run self.resource_version = self._run( File "/usr/local/lib/python3.9/site-packages/airflow/executors/kubernetes_executor.py", line 145, in _run for event in list_worker_pods(): File "/usr/local/lib/python3.9/site-packages/kubernetes/watch/watch.py", line 182, in stream raise client.rest.ApiException( kubernetes.client.exceptions.ApiException: (410) Reason: Expired: too old resource version: 655595751 (655818065) ``` Pods are created and run to completion, but it seems the KubernetesJobWatcher is incapable of seeing that they completed. From there Airflow goes to a complete halt. ### What you expected to happen No errors in the logs and the job watcher does it's job of collecting completed jobs. ### How to reproduce I wish I knew. Trying to downgrade the cncf.kubernetes provider to previous versions to see if it helps. ### Operating System k8s (Airflow images are Debian based) ### Versions of Apache Airflow Providers apache-airflow-providers-amazon 2.6.0 apache-airflow-providers-cncf-kubernetes 3.0.1 apache-airflow-providers-ftp 2.0.1 apache-airflow-providers-http 2.0.2 apache-airflow-providers-imap 2.1.0 apache-airflow-providers-postgres 2.4.0 apache-airflow-providers-sqlite 2.0.1 ### Deployment Other ### Deployment details The deployment is on k8s v1.19.16, made with helm3. ### Anything else This, in the symptoms, look a lot like #17629 but happens in a different place. Redeploying as suggested in that issues seemed to help, but most jobs that were supposed to run last night got stuck again. All jobs use the same pod template, without any customization. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/21087

https://github.com/apache/airflow/pull/23521

cfa95af7e83b067787d8d6596caa3bc97f4b25bd

dee05b2ebca6ab66f1b447837e11fe204f98b2df

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/dagrun.py", "tests/jobs/test_scheduler_job.py"]

A high value of min_file_process_interval & max_active_runs=1 causes stuck dags

### Apache Airflow version 2.2.2 ### What happened When the value of `AIRFLOW__SCHEDULER__MIN_FILE_PROCESS_INTERVAL` is set to 86400, all dags whose `MAX_ACTIVE_RUNS` is set to 1 stop executing & remains stuck forever. If the `MAX_ACTIVE_RUNS` is set to 2 or above, or `AIRFLOW__SCHEDULER__MIN_FILE_PROCESS_INTERVAL` is set to a lower value (bw 30-300), dags work just fine. ### What you expected to happen These 2 settings should be exclusive to each other and there should be no direct impact of one another. ### How to reproduce set AIRFLOW__SCHEDULER__MIN_FILE_PROCESS_INTERVAL to 86400 set MAX_ACTIVE_RUNS to 1 on any dag & observe its execution dates. ### Operating System Debian GNU/Linux 11 (bullseye) ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==1!2.3.0 apache-airflow-providers-cncf-kubernetes==1!2.1.0 apache-airflow-providers-elasticsearch==1!2.1.0 apache-airflow-providers-ftp==1!2.0.1 apache-airflow-providers-google==1!6.1.0 apache-airflow-providers-http==1!2.0.1 apache-airflow-providers-imap==1!2.0.1 apache-airflow-providers-microsoft-azure==1!3.3.0 apache-airflow-providers-mysql==1!2.1.1 apache-airflow-providers-postgres==1!2.3.0 apache-airflow-providers-redis==1!2.0.1 apache-airflow-providers-slack==1!4.1.0 apache-airflow-providers-sqlite==1!2.0.1 apache-airflow-providers-ssh==1!2.3.0 ### Deployment Astronomer ### Deployment details Deployed on AKS via Astronomer Helm chart. ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/21083

https://github.com/apache/airflow/pull/21413

5fbf2471ab4746f5bc691ff47a7895698440d448

feea143af9b1db3b1f8cd8d29677f0b2b2ab757a

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/taskinstance.py", "airflow/utils/state.py"]

Running airflow dags backfill --reset-dagruns <dag_id> -s <execution_start_dt> -e <execution_end_dt> results in error when run twice.

### Apache Airflow version 2.2.3 (latest released) ### What happened It's the same situation as https://github.com/apache/airflow/issues/21023. Only change to `airflow dags backfill` from `airflow dags test`. ``` bash (airflow) [www@np-data-eng-airflow-sync001-lde-jp2v-prod ~]$ airflow dags backfill tutorial --reset-dagruns -s 2022-01-20 -e 2022-01-23 You are about to delete these 9 tasks: <TaskInstance: tutorial.print_date scheduled__2022-01-20T07:48:54.720148+00:00 [success]> <TaskInstance: tutorial.print_date scheduled__2022-01-21T07:48:54.720148+00:00 [success]> <TaskInstance: tutorial.print_date scheduled__2022-01-22T07:48:54.720148+00:00 [success]> <TaskInstance: tutorial.sleep scheduled__2022-01-20T07:48:54.720148+00:00 [success]> <TaskInstance: tutorial.sleep scheduled__2022-01-21T07:48:54.720148+00:00 [success]> <TaskInstance: tutorial.sleep scheduled__2022-01-22T07:48:54.720148+00:00 [success]> <TaskInstance: tutorial.templated scheduled__2022-01-20T07:48:54.720148+00:00 [success]> <TaskInstance: tutorial.templated scheduled__2022-01-21T07:48:54.720148+00:00 [success]> <TaskInstance: tutorial.templated scheduled__2022-01-22T07:48:54.720148+00:00 [success]> Are you sure? (yes/no): y Traceback (most recent call last): File "/home1/www/venv3/airflow/bin/airflow", line 8, in <module> sys.exit(main()) File "/home1/www/venv3/airflow/lib/python3.7/site-packages/airflow/__main__.py", line 48, in main args.func(args) File "/home1/www/venv3/airflow/lib/python3.7/site-packages/airflow/cli/cli_parser.py", line 48, in command return func(*args, **kwargs) File "/home1/www/venv3/airflow/lib/python3.7/site-packages/airflow/utils/cli.py", line 92, in wrapper return f(*args, **kwargs) File "/home1/www/venv3/airflow/lib/python3.7/site-packages/airflow/cli/commands/dag_command.py", line 108, in dag_backfill dag_run_state=State.NONE, File "/home1/www/venv3/airflow/lib/python3.7/site-packages/airflow/models/dag.py", line 1948, in clear_dags dry_run=False, File "/home1/www/venv3/airflow/lib/python3.7/site-packages/airflow/utils/session.py", line 70, in wrapper return func(*args, session=session, **kwargs) File "/home1/www/venv3/airflow/lib/python3.7/site-packages/airflow/models/dag.py", line 1887, in clear dag_run_state=dag_run_state, File "/home1/www/venv3/airflow/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 270, in clear_task_instances dr.state = dag_run_state File "<string>", line 1, in __set__ File "/home1/www/venv3/airflow/lib/python3.7/site-packages/airflow/models/dagrun.py", line 194, in set_state raise ValueError(f"invalid DagRun state: {state}") ValueError: invalid DagRun state: None ``` ### What you expected to happen _No response_ ### How to reproduce 1. Setup Airflow 2.2.3 2. Run any dag (in my case, using [tutorial dag file)](https://airflow.apache.org/docs/apache-airflow/stable/tutorial.html). 3. Run again same dag by `airflow dags backfill` command. ### Operating System CentOS Linux 7.9 ### Versions of Apache Airflow Providers Providers info apache-airflow-providers-celery | 2.1.0 apache-airflow-providers-cncf-kubernetes | 3.0.1 apache-airflow-providers-ftp | 2.0.1 apache-airflow-providers-http | 2.0.2 apache-airflow-providers-imap | 2.1.0 apache-airflow-providers-mysql | 2.1.1 apache-airflow-providers-redis | 2.0.1 apache-airflow-providers-slack | 4.1.0 apache-airflow-providers-sqlite | 2.0.1 apache-airflow-providers-ssh | 2.3.0 ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/21058

https://github.com/apache/airflow/pull/21116

d97e2bac854f9891eb47f0c06c261e89723038ca

d17db3ce8ee8a8a724ced9502c73bc308740a358

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/transfers/dynamodb_to_s3.py"]

Templated fields for DynamoDBToS3Operator similar to MySQLToS3Operator

### Description I am using Airflow to move data periodically to our datalake and noticed that the MySQLToS3Operator has tempalted fields and the DynamoDBToS3Operator doesn't. I found a semi awkward workaround but thought templated fields would be nice. I supposed an implementation could be as simple as adding template_fields = ( 's3_bucket', 's3_key', ) to the Operator similar to the MySQLToS3Operator. Potentially one could also add a log call in execute() as well as using with in the NamedTemporaryFile ### Use case/motivation At the moment MySQLToS3Operator and DynamoDBToS3Operator behave differently in terms of templating and some of the code in MySQLtoS3Operator seems more refined. ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/21057

https://github.com/apache/airflow/pull/22080

c8d49f63ca60fa0fb447768546c2503b746a66dd

a150ee0bc124f21b99fa94adbb16e6ccfe654ae4

python

closed

apache/airflow

https://github.com/apache/airflow

["Dockerfile", "dev/breeze/src/airflow_breeze/build_image/prod/build_prod_params.py"]

More ArtifactsHub specific labels in docker image

### Description We added two labels that allow us to publish docker images in [ArtifactHub](https://artifacthub.io/). We should consider adding other labels as well, if it helps to exposing our image on ArtifactHub. https://artifacthub.io/docs/topics/repositories/#container-images-repositories Probably, we should consider adding these labels with `docker build --label` i.e. as a docker command argument instead of Dockerfile instruction. For now, we should hold off on this change, as the image building process is undergoing a major overhaul - rewriting from Bash to Python. ### Use case/motivation Better exposition of our images on ArtifactHub ### Related issues https://github.com/apache/airflow/pull/21040#issuecomment-1019423007 ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/21046

https://github.com/apache/airflow/pull/23379

451c7cbc42a83a180c4362693508ed33dd1d1dab

5b1ab96865a6e8f18784f88f88f8b17981450cc6

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/views.py", "tests/www/views/test_views.py"]

Recent Tasks mixes status for current and old dag runs

### Apache Airflow version 2.1.4 ### What happened Recent Tasks column in /home dashboard is showing status of the task for different dag runs. See images attached:   ### What you expected to happen As stated in the tooltip, I expect the Recent Tasks column to show the status of tasks for the last run if the DAG isn't currently running, or for the current DAG run (only) if there's one in progress. ### How to reproduce - trigger a manual ran in a dag, make any task fail and get some tasks marked as `failed` and `upstream_failed`. - then trigger the dag again, and see how the Recent Tasks column in /home dashboard is showing tasks failed and upstream_failed from previous run along with running/completed from the current dag run in progress. ### Operating System using apache/airflow:2.1.4-python3.8 image ### Versions of Apache Airflow Providers _No response_ ### Deployment Other Docker-based deployment ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/21036

https://github.com/apache/airflow/pull/21352

28378d867afaac497529bd2e1d2c878edf66f460

28d7bde2750c38300e5cf70ba32be153b1a11f2c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/commands/dag_command.py", "tests/cli/commands/test_dag_command.py"]

Running airflow dags test <dag_id> <execution_dt> results in error when run twice.

### Apache Airflow version main (development) ### What happened # Product and Version Airflow Version: v2.3.0.dev0 (Git Version: .release:2.3.0.dev0+7a9ab1d7170567b1d53938b2f7345dae2026c6ea) to test and learn its functionalities. I am currently installed this using git clone and building the airflow on my MacOS environment, using python3.9. # Problem Statement When I was doing a test on my DAG, I wanted run `airflow dags test <dag_id> <execution_dt>` so that I don't have to use UI to trigger dag runs each time. Running and looking at the result of the dags test proved to be more productive when doing some rapid tests on your DAG. The test runs perfectly for the first time it runs, but when I try to re-run the test again the following error message is observed: ``` [2022-01-21 10:30:33,530] {migration.py:201} INFO - Context impl SQLiteImpl. [2022-01-21 10:30:33,530] {migration.py:204} INFO - Will assume non-transactional DDL. [2022-01-21 10:30:33,568] {dagbag.py:498} INFO - Filling up the DagBag from /Users/howardyoo/airflow/dags [2022-01-21 10:30:33,588] {example_python_operator.py:67} WARNING - The virtalenv_python example task requires virtualenv, please install it. [2022-01-21 10:30:33,594] {tutorial_taskflow_api_etl_virtualenv.py:29} WARNING - The tutorial_taskflow_api_etl_virtualenv example DAG requires virtualenv, please install it. Traceback (most recent call last): File "/Users/howardyoo/python3/bin/airflow", line 33, in <module> sys.exit(load_entry_point('apache-airflow==2.3.0.dev0', 'console_scripts', 'airflow')()) File "/Users/howardyoo/python3/lib/python3.9/site-packages/airflow/__main__.py", line 48, in main args.func(args) File "/Users/howardyoo/python3/lib/python3.9/site-packages/airflow/cli/cli_parser.py", line 50, in command return func(*args, **kwargs) File "/Users/howardyoo/python3/lib/python3.9/site-packages/airflow/utils/session.py", line 71, in wrapper return func(*args, session=session, **kwargs) File "/Users/howardyoo/python3/lib/python3.9/site-packages/airflow/utils/cli.py", line 98, in wrapper return f(*args, **kwargs) File "/Users/howardyoo/python3/lib/python3.9/site-packages/airflow/cli/commands/dag_command.py", line 429, in dag_test dag.clear(start_date=args.execution_date, end_date=args.execution_date, dag_run_state=State.NONE) File "/Users/howardyoo/python3/lib/python3.9/site-packages/airflow/utils/session.py", line 71, in wrapper return func(*args, session=session, **kwargs) File "/Users/howardyoo/python3/lib/python3.9/site-packages/airflow/models/dag.py", line 1906, in clear clear_task_instances( File "/Users/howardyoo/python3/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 286, in clear_task_instances dr.state = dag_run_state File "<string>", line 1, in __set__ File "/Users/howardyoo/python3/lib/python3.9/site-packages/airflow/models/dagrun.py", line 207, in set_state raise ValueError(f"invalid DagRun state: {state}") ValueError: invalid DagRun state: None ``` When going through the DAG runs in my UI, I noticed the following entry on my dag test run.  Looks like when you run the dag with `test` mode, it submits the dag run as `backfill` type. I am not completely sure why the `airflow dags test` would only succeed once, but looks like there might have been some process that may be missing to clear out the test (just my theory). # Workaround A viable workaround to stop it from failing is to find and `deleting` the dag run instance. Once the above dag run entry is deleted, I could successfully run my `airflow dags test` command again. ### What you expected to happen According to the documentation (https://airflow.apache.org/docs/apache-airflow/stable/tutorial.html#id2), it is stated that: > The same applies to airflow dags test [dag_id] [logical_date], but on a DAG level. It performs a single DAG run of the given DAG id. While it does take task dependencies into account, no state is registered in the database. It is convenient for locally testing a full run of your DAG, given that e.g. if one of your tasks expects data at some location, it is available. It does not mention about whether you have to delete the dag run instance to re-run the test, so I would expect that `airflow dags test` command will run successfully, and also successfully on any consecutive runs without any errors. ### How to reproduce - Get the reported version of airflow and install it to run. - Run airflow standalone using `airflow standalone` command. It should start up the basic webserver, scheduler, triggerer to start testing it. - Get any dags that exist in the DAGs. run `airflow dags test <dag_id> <start_dt>` to initiate DAGs test. - Once the test is finished, re-run the command and observe the error. - Go to the DAG runs, delete the dag run that the first run produced, and run the test again - the test should run successfully. ### Operating System MacOS Monterey (Version 12.1) ### Versions of Apache Airflow Providers No providers were used ### Deployment Other ### Deployment details This airflow is running as a `standalone` on my local MacOS environment. I have setup a dev env, by cloning from the github and built the airflow to run locally. It is using sqlite as its backend database, and sequentialExecutor to execute tasks sequentially. ### Anything else Nothing much. I would like this issue to be resolved so that I could run my DAG tests easily without 'actually' running it or relying on the UI. Also, there seems to be little information on what this `test` means and what it is different from the normal runs, so improving documentation to clarify it would be nice. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/21023

https://github.com/apache/airflow/pull/21031

795a6bb066c4f680dcc324758366d59c58eec572

515ea84335fc440fe022db2a0e3b158e0d7702da

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/taskinstance.py", "tests/models/test_taskinstance.py"]

Opening "Task Instance Details" page in web UI unintentionally resets TaskInstance.max_tries

### Apache Airflow version 2.2.3 (latest released) ### What happened `TaskInstance.max_tries` is a cumulative value that's stored in the database. Whenever a failed `TaskInstance` is cleared, its `max_tries` is incremented by `task.retries - 1`. However, the increased `max_tries` value of a `TaskInstance` is reset to the original value (`task.retries`) whenever a user opens the `Task Instance Details` page in the web UI. This is because `www/views.py` queries `TaskInstance` and then calls `refresh_from_task()` on it in a few places, which changes the attributes of the `TaskInstance`. The `session` used in `www/views.py` is then auto committed and the updated values such as `max_tries` are saved to the database. This looks like a bug. So far the only effect I noticed is `max_tries` gets reset causing sensors in "reschedule" mode to timeout prematurely. It happens because of the following lines in `sensors/base.py`. When `max_tries` is reset, this code always gets the `first_try_number` 1 which may have happened a long time ago and thinks the sensor times out. ``` if self.reschedule: # If reschedule, use the start date of the first try (first try can be either the very # first execution of the task, or the first execution after the task was cleared.) first_try_number = context['ti'].max_tries - self.retries + 1 task_reschedules = TaskReschedule.find_for_task_instance( context['ti'], try_number=first_try_number ) ``` ### What you expected to happen Opening "Task Instance Details" page should not make any changes to the state of the TaskInstance in the database. ### How to reproduce Create a DAG like this: ``` from datetime import datetime from airflow import DAG from airflow.sensors.python import PythonSensor with DAG( dag_id="reschedule_clear", start_date=datetime(2021, 1, 1), catchup=False, schedule_interval="@daily", ) as dag: task_1 = PythonSensor( task_id="task_1", python_callable=lambda: False, mode="reschedule", timeout=90, retries=5, ) ``` ``` State of the TaskInstance after it failed for the first time: airflow=# select max_tries, try_number from task_instance ; max_tries | try_number -----------+------------ 5 | 1 (1 row) After the failed TaskInstance was cleared: airflow=# select max_tries, try_number from task_instance ; max_tries | try_number -----------+------------ 6 | 1 (1 row) After the TaskInstance failed for a second time: airflow=# select max_tries, try_number from task_instance ; max_tries | try_number -----------+------------ 6 | 2 (1 row) After the TaskInstance is cleared a second time: airflow=# select max_tries, try_number from task_instance ; max_tries | try_number -----------+------------ 7 | 2 (1 row) After user opens the "Task Instance Details" page in Web UI: airflow=# select max_tries, try_number from task_instance ; max_tries | try_number -----------+------------ 5 | 2 (1 row) ``` This is the exception raised when the `max_tries` was accidentally changed from 7 to 5. Note these two lines when the total attempt changed from 8 to 6 after the user clicked on "Task Instance Details": ``` [2022-01-21, 13:57:23 UTC] {taskinstance.py:1274} INFO - Starting attempt 3 of 8 ... [2022-01-21, 13:58:25 UTC] {taskinstance.py:1274} INFO - Starting attempt 3 of 6 ``` ``` -------------------------------------------------------------------------------- [2022-01-21, 13:57:23 UTC] {taskinstance.py:1274} INFO - Starting attempt 3 of 8 [2022-01-21, 13:57:23 UTC] {taskinstance.py:1275} INFO - ... [2022-01-21, 13:57:23 UTC] {taskinstance.py:1724} INFO - Rescheduling task, marking task as UP_FOR_RESCHEDULE [2022-01-21, 13:57:23 UTC] {local_task_job.py:156} INFO - Task exited with return code 0 ... -------------------------------------------------------------------------------- [2022-01-21, 13:58:25 UTC] {taskinstance.py:1274} INFO - Starting attempt 3 of 6 [2022-01-21, 13:58:25 UTC] {taskinstance.py:1275} INFO - -------------------------------------------------------------------------------- [2022-01-21, 13:58:25 UTC] {taskinstance.py:1294} INFO - Executing <Task(PythonSensor): task_1> on 2022-01-20 00:00:00+00:00 [2022-01-21, 13:58:25 UTC] {standard_task_runner.py:52} INFO - Started process 17578 to run task [2022-01-21, 13:58:25 UTC] {standard_task_runner.py:76} INFO - Running: ['***', 'tasks', 'run', 'reschedule_clear', 'task_1', 'scheduled__2022-01-20T00:00:00+00:00', '--job-id', '9', '--raw', '--subdir', '/opt/***/***/example_dags/reschedule_clear.py', '--cfg-path', '/tmp/tmpmkyuigjb', '--error-file', '/tmp/tmpvvtn1w1z'] [2022-01-21, 13:58:25 UTC] {standard_task_runner.py:77} INFO - Job 9: Subtask task_1 [2022-01-21, 13:58:25 UTC] {logging_mixin.py:115} INFO - Running <TaskInstance: reschedule_clear.task_1 scheduled__2022-01-20T00:00:00+00:00 [running]> on host f9e8f282cff4 [2022-01-21, 13:58:25 UTC] {logging_mixin.py:115} WARNING - /opt/***/***/models/taskinstance.py:839 DeprecationWarning: Passing 'execution_date' to 'XCom.clear()' is deprecated. Use 'run_id' instead. [2022-01-21, 13:58:25 UTC] {taskinstance.py:1459} INFO - Exporting the following env vars: AIRFLOW_CTX_DAG_OWNER=*** AIRFLOW_CTX_DAG_ID=reschedule_clear AIRFLOW_CTX_TASK_ID=task_1 AIRFLOW_CTX_EXECUTION_DATE=2022-01-20T00:00:00+00:00 AIRFLOW_CTX_DAG_RUN_ID=scheduled__2022-01-20T00:00:00+00:00 [2022-01-21, 13:58:25 UTC] {python.py:70} INFO - Poking callable: <function <lambda> at 0x7fc2eb01b3a0> [2022-01-21, 13:58:25 UTC] {taskinstance.py:1741} ERROR - Task failed with exception Traceback (most recent call last): File "/opt/airflow/airflow/models/taskinstance.py", line 1364, in _run_raw_task self._execute_task_with_callbacks(context) File "/opt/airflow/airflow/models/taskinstance.py", line 1490, in _execute_task_with_callbacks result = self._execute_task(context, self.task) File "/opt/airflow/airflow/models/taskinstance.py", line 1546, in _execute_task result = execute_callable(context=context) File "/opt/airflow/airflow/sensors/base.py", line 265, in execute raise AirflowSensorTimeout(f"Snap. Time is OUT. DAG id: {log_dag_id}") airflow.exceptions.AirflowSensorTimeout: Snap. Time is OUT. DAG id: reschedule_clear [2022-01-21, 13:58:25 UTC] {taskinstance.py:1302} INFO - Immediate failure requested. Marking task as FAILED. dag_id=reschedule_clear, task_id=task_1, execution_date=20220120T000000, start_date=20220121T135825, end_date=20220121T135825 [2022-01-21, 13:58:25 UTC] {standard_task_runner.py:89} ERROR - Failed to execute job 9 for task task_1 Traceback (most recent call last): File "/opt/airflow/airflow/task/task_runner/standard_task_runner.py", line 85, in _start_by_fork args.func(args, dag=self.dag) File "/opt/airflow/airflow/cli/cli_parser.py", line 49, in command return func(*args, **kwargs) File "/opt/airflow/airflow/utils/cli.py", line 98, in wrapper return f(*args, **kwargs) File "/opt/airflow/airflow/cli/commands/task_command.py", line 339, in task_run _run_task_by_selected_method(args, dag, ti) File "/opt/airflow/airflow/cli/commands/task_command.py", line 147, in _run_task_by_selected_method _run_raw_task(args, ti) File "/opt/airflow/airflow/cli/commands/task_command.py", line 220, in _run_raw_task ti._run_raw_task( File "/opt/airflow/airflow/utils/session.py", line 71, in wrapper return func(*args, session=session, **kwargs) File "/opt/airflow/airflow/models/taskinstance.py", line 1364, in _run_raw_task self._execute_task_with_callbacks(context) File "/opt/airflow/airflow/models/taskinstance.py", line 1490, in _execute_task_with_callbacks result = self._execute_task(context, self.task) File "/opt/airflow/airflow/models/taskinstance.py", line 1546, in _execute_task result = execute_callable(context=context) File "/opt/airflow/airflow/sensors/base.py", line 265, in execute raise AirflowSensorTimeout(f"Snap. Time is OUT. DAG id: {log_dag_id}") airflow.exceptions.AirflowSensorTimeout: Snap. Time is OUT. DAG id: reschedule_clear [2022-01-21, 13:58:25 UTC] {local_task_job.py:156} INFO - Task exited with return code 1 [2022-01-21, 13:58:25 UTC] {local_task_job.py:273} INFO - 0 downstream tasks scheduled from follow-on schedule check ``` ### Operating System Any ### Versions of Apache Airflow Providers Any ### Deployment Virtualenv installation ### Deployment details Any ### Anything else Every time a user opens "Task Instance Details" page. ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/21017

https://github.com/apache/airflow/pull/21018

0d623296f1e5b6354e37dc0a45b2e4ed1a13901e

e3832a77a3e0d374dfdbe14f34a941d22c9c459d

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/helm-chart/index.rst"]

ArgoCD deployment: build in redis is not restarted on password change

### Official Helm Chart version 1.4.0 (latest released) ### Apache Airflow version 2.2.3 (latest released) ### Kubernetes Version 1.21.x,1.18.x ### Helm Chart configuration KubernetesCeleryExecutor used ### Docker Image customisations _No response_ ### What happened On each argocd airflow app update ```{{ .Release.Name }}-redis-password``` and ```{{ .Release.Name }}-broker-url``` is regenerated(since argocd do not honor ```"helm.sh/hook": "pre-install"```). airflow pods restarted(as expected),get new redis connection and connection start failing with WRONG_PASSWORD, since redis is not restarted(old password used). Redis in chart 1.4 have no health checks. ### What you expected to happen Generally, I have two options: 1. (Prefered) Add health check to Redis with login sequence. The password should be updated "on the fly"(read from the mounted secret and try to connect) 2. (Workeraund) I implemented a workaround with the parent chart. ```{{ .Release.Name }}-Redis-password``` and ```{{ .Release.Name }}-broker-url``` secrets generated from template where ```immutable: true``` added to the secret definition. ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20999

https://github.com/apache/airflow/pull/29078

30ad26e705f50442f05dd579990372196323fc86

6c479437b1aedf74d029463bda56b42950278287

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/dag.py", "tests/models/test_dag.py"]

DagFileProcessor 'NoneType' is not iterable

### Apache Airflow version 2.2.2 ### What happened I'm seeing the same log repeating in the Scheduler. I'm working in a restricted network so I cannot bring the entire log: ``` in bulk_write_to_db if orm_tag.name not in set(dag.tags) TypeError: 'NoneType' object is not iterable ``` I saw that a single DAG didn't have any labels and i tried to add a label but the log is still showing ### What you expected to happen _No response_ ### How to reproduce _No response_ ### Operating System Debian 10 (Scheduler image) ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details I'm deploying on OpenShift 4.8 using the official Helm Chart v1.3.0 ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20993

https://github.com/apache/airflow/pull/21757

768d851ca995bbe46cfdaeed7c46a51201b723e2

d7265791187fb2117dfd090cdb7cce3f8c20866c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/elasticsearch/log/es_task_handler.py", "airflow/utils/log/file_task_handler.py"]

Elasticsearch remote log will not fetch task logs from manual dagruns before 2.2 upgrade

### Apache Airflow Provider(s) elasticsearch ### Versions of Apache Airflow Providers ``` apache-airflow-providers-amazon==1!2.5.0 apache-airflow-providers-cncf-kubernetes==1!2.1.0 apache-airflow-providers-datadog==1!2.0.1 apache-airflow-providers-elasticsearch==1!2.1.0 apache-airflow-providers-ftp==1!2.0.1 apache-airflow-providers-google==1!6.1.0 apache-airflow-providers-http==1!2.0.1 apache-airflow-providers-imap==1!2.0.1 apache-airflow-providers-microsoft-azure==1!3.3.0 apache-airflow-providers-mysql==1!2.1.1 apache-airflow-providers-postgres==1!2.3.0 apache-airflow-providers-redis==1!2.0.1 apache-airflow-providers-slack==1!4.1.0 apache-airflow-providers-sqlite==1!2.0.1 apache-airflow-providers-ssh==1!2.3.0 ``` ### Apache Airflow version 2.2.2 ### Operating System Debian Bullseye ### Deployment Astronomer ### Deployment details _No response_ ### What happened After upgrading to 2.2, task logs from manual dagruns performed before the upgrade could no longer be retrieved, even though they can still be seen in Kibana. Scheduled dagruns' tasks and tasks for dagruns begun after the upgrade are retrieved without issue. The issue appears to be because these tasks with missing logs all belong to dagruns that do not have the attribute data_interval_start or data_interval_end set. ### What you expected to happen Task logs continue to be fetched after upgrade. ### How to reproduce Below is how I verified the log fetching process. I ran the code snippet in a python interpreter in the scheduler to test log fetching. ```py from airflow.models import TaskInstance, DagBag, DagRun from airflow.settings import Session, DAGS_FOLDER from airflow.configuration import conf import logging from dateutil import parser logger = logging.getLogger('airflow.task') task_log_reader = conf.get('logging', 'task_log_reader') handler = next((handler for handler in logger.handlers if handler.name == task_log_reader), None) dag_id = 'pipeline_nile_reconciliation' task_id = 'nile_overcount_spend_resolution_task' execution_date = parser.parse('2022-01-10T11:49:57.197933+00:00') try_number=1 session = Session() ti = session.query(TaskInstance).filter( TaskInstance.dag_id == dag_id, TaskInstance.task_id == task_id, TaskInstance.execution_date == execution_date).first() dagrun = session.query(DagRun).filter( DagRun.dag_id == dag_id, DagRun.execution_date == execution_date).first() dagbag = DagBag(DAGS_FOLDER, read_dags_from_db=True) dag = dagbag.get_dag(dag_id) ti.task = dag.get_task(ti.task_id) ti.dagrun = dagrun handler.read(ti, try_number, {}) ``` The following error log indicates errors in the log reading. ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.9/site-packages/airflow/utils/log/file_task_handler.py", line 239, in read log, metadata = self._read(task_instance, try_number_element, metadata) File "/usr/local/lib/python3.9/site-packages/airflow/providers/elasticsearch/log/es_task_handler.py", line 168, in _read log_id = self._render_log_id(ti, try_number) File "/usr/local/lib/python3.9/site-packages/airflow/providers/elasticsearch/log/es_task_handler.py", line 107, in _render_log_id data_interval_start = self._clean_date(dag_run.data_interval_start) File "/usr/local/lib/python3.9/site-packages/airflow/providers/elasticsearch/log/es_task_handler.py", line 134, in _clean_date return value.strftime("%Y_%m_%dT%H_%M_%S_%f") AttributeError: 'NoneType' object has no attribute 'strftime' ``` ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20991

https://github.com/apache/airflow/pull/21289

c9fdcdbc72543219314430e3de712d69033c57cf

44bd211b19dcb75eeb53ced5bea2cf0c80654b1a

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/hooks/subprocess.py", "airflow/providers/cncf/kubernetes/utils/pod_manager.py", "tests/hooks/test_subprocess.py"]

Exception when parsing log

### Apache Airflow version 2.1.4 ### What happened [2022-01-19 13:42:46,107] {taskinstance.py:1463} ERROR - Task failed with exception Traceback (most recent call last): File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1165, in _run_raw_task self._prepare_and_execute_task_with_callbacks(context, task) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1283, in _prepare_and_execute_task_with_callbacks result = self._execute_task(context, task_copy) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1313, in _execute_task result = task_copy.execute(context=context) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/cncf/kubernetes/operators/kubernetes_pod.py", line 367, in execute final_state, remote_pod, result = self.create_new_pod_for_operator(labels, launcher) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/cncf/kubernetes/operators/kubernetes_pod.py", line 521, in create_new_pod_for_operator final_state, remote_pod, result = launcher.monitor_pod(pod=self.pod, get_logs=self.get_logs) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/cncf/kubernetes/utils/pod_launcher.py", line 148, in monitor_pod timestamp, message = self.parse_log_line(line.decode('utf-8')) UnicodeDecodeError: 'utf-8' codec can't decode bytes in position 16413-16414: invalid continuation byte ### What you expected to happen _No response_ ### How to reproduce _No response_ ### Operating System Debian GNU/Linux 10 (buster) ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==2.2.0 apache-airflow-providers-celery==2.0.0 apache-airflow-providers-cncf-kubernetes==2.0.2 apache-airflow-providers-docker==2.1.1 apache-airflow-providers-elasticsearch==2.0.3 apache-airflow-providers-ftp==2.0.1 apache-airflow-providers-google==5.1.0 apache-airflow-providers-grpc==2.0.1 apache-airflow-providers-hashicorp==2.1.0 apache-airflow-providers-http==2.0.1 apache-airflow-providers-imap==2.0.1 apache-airflow-providers-microsoft-azure==3.1.1 apache-airflow-providers-mysql==2.1.1 apache-airflow-providers-postgres==2.2.0 apache-airflow-providers-redis==2.0.1 apache-airflow-providers-sendgrid==2.0.1 apache-airflow-providers-sftp==2.1.1 apache-airflow-providers-slack==4.0.1 apache-airflow-providers-sqlite==2.0.1 apache-airflow-providers-ssh==2.1.1 ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else Always on a specific docker container. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20966

https://github.com/apache/airflow/pull/23301

c5b72bf30c8b80b6c022055834fc7272a1a44526

863b2576423e1a7933750b297a9b4518ae598db9

python

closed

apache/airflow

https://github.com/apache/airflow

["scripts/docker/install_mysql.sh"]

Apt MySQL Client fails to install due to incorrect GPG Key

### Apache Airflow version 2.0.2 ### What happened When attempting to rebuild an apache airflow image, we are getting failures during our builds when trying to run `apt-get update`. The error we see: ``` #5 3.879 W: GPG error: http://repo.mysql.com/apt/debian buster InRelease: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY 467B942D3A79BD29 #5 3.879 E: The repository 'http://repo.mysql.com/apt/debian buster InRelease' is not signed. ``` ### What you expected to happen This behaviour shouldn't occur but it looks like some changes were published to the `mysql` apt repository this morning which is when we started experiencing issues. <img width="552" alt="Screen Shot 2022-01-17 at 5 17 49 PM" src="https://user-images.githubusercontent.com/40478775/149842772-78390787-5556-409e-9ab7-67f1260ec48a.png"> I think we just need to update the [airflow script](https://github.com/apache/airflow/blob/2.0.2/scripts/docker/install_mysql.sh ) which installs mysql and has a key already hardcoded [here](https://github.com/apache/airflow/blob/10023fdd65fa78033e7125d3d8103b63c127056e/scripts/docker/install_mysql.sh#L42). ### How to reproduce Create a `Dockerfile`. Add the following lines to the Dockerfile: ``` FROM apache/airflow:2.0.2-python3.7 as airflow_main USER root RUN apt-get update ``` In a shell run command -> `docker build .` ### Operating System Debian 10 ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else We were able to fix this by running the following command before the `apt-get update` ``` FROM apache/airflow:2.0.2-python3.7 as airflow_main USER root RUN sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 467B942D3A79BD29 RUN apt-get update ``` ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20911

https://github.com/apache/airflow/pull/20912

ddedda420d451866cf177c1eb8efa8d8b9b9f78d

7e29506037fa543f5d9b438320db064cdb820c7b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/transfers/gcs_to_local.py", "tests/providers/google/cloud/transfers/test_gcs_to_local.py"]

Bytes cast to String in `airflow.providers.google.cloud.transfers.gcs_to_local.GCSToLocalFilesystemOperator` ~142

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers ``` $ pip freeze | grep apache-airflow-providers apache-airflow-providers-ftp==2.0.1 apache-airflow-providers-google==6.3.0 apache-airflow-providers-http==2.0.2 apache-airflow-providers-imap==2.1.0 apache-airflow-providers-pagerduty==2.1.0 apache-airflow-providers-sftp==2.4.0 apache-airflow-providers-sqlite==2.0.1 apache-airflow-providers-ssh==2.3.0 ``` ### Apache Airflow version 2.2.3 (latest released) ### Operating System Ubuntu 20.04.3 LTS ### Deployment Composer ### Deployment details _No response_ ### What happened Using `airflow.providers.google.cloud.transfers.gcs_to_local.GCSToLocalFilesystemOperator` to load the contents of a file into `xcom` unexpectedly casts the file bytes to string. ### What you expected to happen `GCSToLocalFilesystemOperator` should not cast to string ### How to reproduce Store a file on gcs; ``` Hello World! ``` Read file to xcom ``` my_task = GCSToLocalFilesystemOperator( task_id='my_task', bucket=bucket, object_name=object_path, store_to_xcom_key='my_xcom_key', ) ``` Access via jinja; ``` {{ ti.xcom_pull(task_ids="my_task", key="my_xcom_key") }} ``` XCom result is; ``` b'Hello World!' ``` ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20901

https://github.com/apache/airflow/pull/20919

b171e03924fba92924162563f606d25f0d75351e

b8526abc2c220b1e07eed83694dfee972c2e2609

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/config_templates/config.yml", "airflow/config_templates/default_airflow.cfg", "airflow/www/views.py", "docs/apache-airflow/howto/customize-ui.rst", "tests/www/views/test_views_base.py"]

Allow for Markup in UI page title

### Description A custom page title can be set on the UI with the `instance_name` variable in `airflow.cfg`. It would be nice to have the option to include Markup text in that variable for further customization of the title, similar to how dashboard alerts introduced in #18284 allow for `html=True`. ### Use case/motivation It would be useful to be able to use formatting like underline, bold, color, etc, in the UI title. For example, color-coded environments to minimize chances of a developer triggering a DAG in the wrong environment:  ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20877

https://github.com/apache/airflow/pull/20888

75755d7f65fb06c6e2e74f805b877774bfa7fcda

a65555e604481388da40cea561ca78f5cabb5f50

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/migrations/versions/e655c0453f75_add_taskmap_and_map_id_on_taskinstance.py", "airflow/models/taskinstance.py", "airflow/models/taskreschedule.py"]

Airflow database upgrade fails with "psycopg2.errors.NotNullViolation: column "map_index" of relation "task_instance" contains null value"s

### Apache Airflow version main (development) ### What happened I currently have Airflow 2.2.3 and due to this [issue](https://github.com/apache/airflow/issues/19699) I have tried to upgrade Airflow to this [commit](https://github.com/apache/airflow/commit/14ee831c7ad767e31a3aeccf3edbc519b3b8c923). When I run `airflow db upgrade` I get the following error: ``` INFO [alembic.runtime.migration] Context impl PostgresqlImpl. INFO [alembic.runtime.migration] Will assume transactional DDL. INFO [alembic.runtime.migration] Running upgrade 587bdf053233 -> e655c0453f75, Add TaskMap and map_index on TaskInstance. Traceback (most recent call last): File "/usr/local/lib/python3.7/dist-packages/sqlalchemy/engine/base.py", line 1277, in _execute_context cursor, statement, parameters, context File "/usr/local/lib/python3.7/dist-packages/sqlalchemy/engine/default.py", line 608, in do_execute cursor.execute(statement, parameters) psycopg2.errors.NotNullViolation: column "map_index" of relation "task_instance" contains null values ``` The map_index column was introduced with this [PR](https://github.com/apache/airflow/pull/20286). Could you please advise? ### What you expected to happen _No response_ ### How to reproduce _No response_ ### Operating System Ubuntu 18.04.6 LTS ### Versions of Apache Airflow Providers _No response_ ### Deployment Other ### Deployment details Kubernetes ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20876

https://github.com/apache/airflow/pull/20902

7e29506037fa543f5d9b438320db064cdb820c7b

66276e68ba37abb2991cb0e03ca93c327fc63a09

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/css/bootstrap-theme.css"]

Distinguish links in DAG documentation from code

### Description Currently code blocks (i.e using backticks in markdown) look the same as links when using `dag.doc_md`. This makes it very difficult to distinguish what is clickable in links. For example in the image below,  `document-chunker` is a code block, whereas `offline-processing-storage-chunking-manual-trigger-v5` is a link - but they appear identical. If this was rendered on github it'd look like: > The restored files must subsequently be restored using a manual trigger of `document-chunker`: [offline-processing-storage-chunking-manual-trigger-v5](https://invalid-link.com), set `s3_input_base_path` as copied and provide the same start and end dates. notice that the link is clearly different to the code block. We're using Airflow version 2.1.4. ### Use case/motivation We're trying to link between manual steps which are possible with Airflow runs for recovery, reducing the need to write long pieces of documentation. The links are currently difficult to distinguish which causes issues when following instructions. ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20873

https://github.com/apache/airflow/pull/20938

892204105154fdc520758e66512fb6021d404e57

cdb120de2403f5a21aa6d84d10f68c1b7f086aba

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/views.py", "tests/www/views/test_views_connection.py"]

Cannot edit custom fields on provider connections

### Apache Airflow version 2.2.3 (latest released) ### What happened Connections from providers are not saving edited values in any custom connection forms. You can work around the issue by changing connection type to something like HTTP, and modifying the extra field's JSON. ### What you expected to happen _No response_ ### How to reproduce Using the official docker compose deployment, add a new connection of type 'Azure Data Explorer' and fill out one of the custom connection fields (e.g., "Tenant ID"). Save the record. Edit the record and enter a new value for the same field or any other field that is defined in the associated Hook's `get_connection_form_widgets` function. Save the record. Edit again. The changes were not saved. ### Operating System Windows 10, using docker ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20839

https://github.com/apache/airflow/pull/20883

bad070f7f484a9b4065a0d86195a1e8002d9bfef

44df1420582b358594c8d7344865811cff02956c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/hooks/glue.py", "tests/providers/amazon/aws/hooks/test_glue.py"]

Unable to specify Python version for AwsGlueJobOperator

### Apache Airflow Provider(s) amazon ### Versions of Apache Airflow Providers _No response_ ### Apache Airflow version 2.0.2 ### Operating System Amazon Linux ### Deployment MWAA ### Deployment details _No response_ ### What happened When a new Glue job is created using the AwsGlueJobOperator, the job is defaulting to Python2. Setting the version in create_job_kwargs fails with key error. ### What you expected to happen Expected the Glue job to be created with a Python3 runtime. create_job_kwargs are passed to the boto3 glue client create_job method which includes a "Command" parameter that is a dictionary containing the Python version. ### How to reproduce Create a dag with an AwsGlueJobOperator and pass a "Command" parameter in the create_job_kwargs argument. ``` create_glue_job_args = { "Command": { "Name": "abalone-preprocess", "ScriptLocation": f"s3://{output_bucket}/code/preprocess.py", "PythonVersion": "3" } } glue_etl = AwsGlueJobOperator( task_id="glue_etl", s3_bucket=output_bucket, script_args={ '--S3_INPUT_BUCKET': data_bucket, '--S3_INPUT_KEY_PREFIX': 'input/raw', '--S3_UPLOADS_KEY_PREFIX': 'input/uploads', '--S3_OUTPUT_BUCKET': output_bucket, '--S3_OUTPUT_KEY_PREFIX': str(determine_dataset_id.output) +'/input/data' }, iam_role_name="MLOps", retry_limit=2, concurrent_run_limit=3, create_job_kwargs=create_glue_job_args, dag=dag) ``` ``` [2022-01-04 16:43:42,053] {{logging_mixin.py:104}} INFO - [2022-01-04 16:43:42,053] {{glue.py:190}} ERROR - Failed to create aws glue job, error: 'Command' [2022-01-04 16:43:42,081] {{logging_mixin.py:104}} INFO - [2022-01-04 16:43:42,081] {{glue.py:112}} ERROR - Failed to run aws glue job, error: 'Command' [2022-01-04 16:43:42,101] {{taskinstance.py:1482}} ERROR - Task failed with exception Traceback (most recent call last): File "/usr/local/lib/python3.7/site-packages/airflow/providers/amazon/aws/hooks/glue.py", line 166, in get_or_create_glue_job get_job_response = glue_client.get_job(JobName=self.job_name) File "/usr/local/lib/python3.7/site-packages/botocore/client.py", line 357, in _api_call return self._make_api_call(operation_name, kwargs) File "/usr/local/lib/python3.7/site-packages/botocore/client.py", line 676, in _make_api_call raise error_class(parsed_response, operation_name) botocore.errorfactory.EntityNotFoundException: An error occurred (EntityNotFoundException) when calling the GetJob operation: Job with name: abalone-preprocess not found. During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/usr/local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1138, in _run_raw_task self._prepare_and_execute_task_with_callbacks(context, task) File "/usr/local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1311, in _prepare_and_execute_task_with_callbacks result = self._execute_task(context, task_copy) File "/usr/local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1341, in _execute_task result = task_copy.execute(context=context) File "/usr/local/lib/python3.7/site-packages/airflow/providers/amazon/aws/operators/glue.py", line 121, in execute glue_job_run = glue_job.initialize_job(self.script_args) File "/usr/local/lib/python3.7/site-packages/airflow/providers/amazon/aws/hooks/glue.py", line 108, in initialize_job job_name = self.get_or_create_glue_job() File "/usr/local/lib/python3.7/site-packages/airflow/providers/amazon/aws/hooks/glue.py", line 186, in get_or_create_glue_job **self.create_job_kwargs, KeyError: 'Command' ``` ### Anything else When a new job is being created. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20832

https://github.com/apache/airflow/pull/24215

fd4e344880b505cfe53a97a6373d329515bbc7a3

41898d89220c8525d941367a875b4806e618b0d0

python

closed

apache/airflow

https://github.com/apache/airflow

["STATIC_CODE_CHECKS.rst"]

Static check docs mistakes in example

### Describe the issue with documentation <img width="1048" alt="Screenshot 2022-01-12 at 4 22 44 PM" src="https://user-images.githubusercontent.com/10162465/149127114-5810f86e-83eb-40f6-b438-5b18b7026e86.png"> Run the flake8 check for the tests.core package with verbose output: ./breeze static-check mypy -- --files tests/hooks/test_druid_hook.py The doc says flake8 check for tests.core package but it runs mypy check for files ### How to solve the problem It can be solved by adding the right instruction. ./breeze static-check flake8 -- --files tests/core/* --verbose Didn't check if the above command is working. But we have to use similar command like above. ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20823

https://github.com/apache/airflow/pull/20844

8dc68d47048d559cf4b76874d8d5e7a5af6359b6

c49d6ec8b67e48d0c0fba1fe30c00f590f88ae65

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/dag_processing/manager.py", "airflow/sensors/smart_sensor.py", "tests/dag_processing/test_manager.py", "tests/sensors/test_smart_sensor_operator.py"]

Some timing metrics are in seconds but reported as milliseconds

### Apache Airflow version 2.2.2 ### What happened When Airflow reports timing stats it uses either a `timedelta` or a direct value. When using `timedelta` it is converted automatically to the correct units of measurement but when using a direct value it is accepted to already be in the correct units. Unfortunately the Stats class, either being statsd.StatsClient or a stub, expects *milliseconds* while the Airflow code passes the value in *seconds*. The result is two of the timing metrics are wrong by a magnitude of 1000. This affects `dag_processing.last_duration.<dag_file>` and `smart_sensor_operator.loop_duration`. The rest either pass `timedelta` or use a `Stats.timer` which calculates timing on its own and is not affected. ### What you expected to happen All timing metrics to be in the correct unit of measurement. ### How to reproduce Run a statsd-exporter and a prometheus to collect the metrics and compare to the logs. For the dag processing metric, the scheduler logs the amounts and can be directly compared to the gathered metric. ### Operating System Linux ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details Using these two with the configs below to process the metrics. The metrics can be viewed in the prometheus UI on `localhost:9090`. ``` prometheus: image: prom/prometheus:v2.32.1 command: - --config.file=/etc/prometheus/config.yml - --web.console.libraries=/usr/share/prometheus/console_libraries - --web.console.templates=/usr/share/prometheus/consoles ports: - 9090:9090 volumes: - ./prometheus:/etc/prometheus statsd-exporter: image: prom/statsd-exporter:v0.22.4 command: - --statsd.mapping-config=/tmp/statsd_mapping.yml ports: - 9102:9102 - 9125:9125 - 9125:9125/udp volumes: - ./prometheus/statsd_mapping.yml:/tmp/statsd_mapping.yml ``` The prometheus config is: ``` global: scrape_interval: 15s scrape_configs: - job_name: airflow_statsd scrape_interval: 1m scrape_timeout: 30s static_configs: - targets: - statsd-exporter:9102 ``` The metrics mapping for statsd-exporter is: ``` mappings: - match: "airflow.dag_processing.last_duration.*" name: "airflow_dag_processing_last_duration" labels: dag_file: "$1" - match: "airflow.collect_db_tags" name: "airflow_collect_db_tags" labels: {} - match: "airflow.scheduler.critical_section_duration" name: "airflow_scheduler_critical_section_duration" labels: {} - match: "airflow.dagrun.schedule_delay.*" name: "airflow_dagrun_schedule_delay" labels: dag_id: "$1" - match: "airflow.dag_processing.total_parse_time" name: "airflow_dag_processing_total_parse_time" labels: {} - match: "airflow.dag_processing.last_run.seconds_ago.*" name: "airflow_dag_processing_last_run_seconds_ago" labels: dag_file: "$1" ``` ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20804

https://github.com/apache/airflow/pull/21106

6e96f04eb515149f185448b8dfb84813c5879fc0

1507ca48d7c211799129ce7956c11f4c45fee5bc

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/hooks/emr.py"]

Airflow Elastic MapReduce connection

### Description Airflow has connections: ``` Amazon Web Services Amazon Redshift Elastic MapReduce ``` It wasn't easy to find the `Elastic MapReduce` because it wasn't listed as Amazon. I think it would be better to rename it to `Amazon Elastic MapReduce` for easier find in the Connections droplist ### Use case/motivation _No response_ ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20746

https://github.com/apache/airflow/pull/20767

da9210e89c618611b1e450617277b738ce92ffd7

88e3f2ae5e5101928858099f9d4e7fb6542c4110

python

closed

apache/airflow

https://github.com/apache/airflow

[".github/workflows/build-images.yml", "breeze", "dev/breeze/src/airflow_breeze/breeze.py", "dev/breeze/src/airflow_breeze/utils/path_utils.py"]

CI: Reuse Breeze CI image building for CI

We already have a basic Build image function in `Breeze` - we should be able to use it to build images in CI. There are actions that build images in `./build-imaages.yml` and they should simply (similarely to free-space) use Python commands develped in ./dev/breeze.

https://github.com/apache/airflow/issues/20741

https://github.com/apache/airflow/pull/22008

60ec508945277215c557b7666cdb97872facdae2

eddf4a30f81c40ada1d8805d689d82c3ac70e311

python

closed

apache/airflow

https://github.com/apache/airflow

[".pre-commit-config.yaml", "dev/breeze/src/airflow_breeze/breeze.py", "dev/breeze/src/airflow_breeze/global_constants.py", "dev/breeze/src/airflow_breeze/pre_commit_ids.py", "dev/breeze/src/airflow_breeze/pre_commit_ids_TEMPLATE.py.jinja2", "dev/breeze/src/airflow_breeze/utils/run_utils.py", "dev/breeze/tests/test_cache.py", "scripts/ci/pre_commit/pre_commit_check_pre_commit_hook_names.py"]

Breeze: Running static checks with Breeze

We should rewrite the action to run static checks with Breeze. Currently implemented by 'static-checks' opetoin. The should baiscally run appropriate `pre-commit run` statement. The difference vs. running just pre-commit is that it should implement auto-complete of checks available (pre-commit does not have it) and make sure that CI image is built for the checks that require it. The list of available static checks should be retrieved by parsing the .pre-commit.yml file rather than (as it is currently done) maintaining the list in ./breeze-complete script More info on static checks: https://github.com/apache/airflow/blob/main/STATIC_CODE_CHECKS.rst

https://github.com/apache/airflow/issues/20740

https://github.com/apache/airflow/pull/20848

82adce535eb0c427c230035d648bf3c829824b21

684fe46158aa3d6cb2de245d29e20e487d8f2158

python

closed

apache/airflow

https://github.com/apache/airflow

["dev/breeze/src/airflow_breeze/breeze.py", "dev/breeze/src/airflow_breeze/cache.py", "dev/breeze/src/airflow_breeze/docs_generator/__init__.py", "dev/breeze/src/airflow_breeze/docs_generator/build_documentation.py", "dev/breeze/src/airflow_breeze/docs_generator/doc_builder.py", "dev/breeze/src/airflow_breeze/global_constants.py", "dev/breeze/src/airflow_breeze/utils/docker_command_utils.py", "dev/breeze/tests/test_commands.py", "dev/breeze/tests/test_global_constants.py"]

Breeze: Build documentation with Breeze

The "build-documentation" action should be rewritten in python in the new Breeze2 command. It should allow for the same parameters that are already used by the https://github.com/apache/airflow/blob/main/docs/build_docs.py script: `--package-filter` for example. It shoudl basically run the ./build_docs.py using CI image. Also we should add `airflow-start-doc-server` as a separate entrypoint (similar to airflow-freespace).

https://github.com/apache/airflow/issues/20739

https://github.com/apache/airflow/pull/20886

793684a88ce3815568c585c45eb85a74fa5b2d63

81c85a09d944021989ab530bc6caf1d9091a753c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/sftp/hooks/sftp.py", "tests/providers/sftp/hooks/test_sftp.py", "tests/providers/sftp/hooks/test_sftp_outdated.py"]

SFTPHook uses default connection 'sftp_default' even when ssh_conn_id is passed

### Apache Airflow Provider(s) sftp ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==2.6.0 apache-airflow-providers-apache-hdfs==2.2.0 apache-airflow-providers-apache-hive==2.1.0 apache-airflow-providers-apache-livy==2.1.0 apache-airflow-providers-apache-spark==2.0.3 apache-airflow-providers-celery==2.1.0 apache-airflow-providers-elasticsearch==2.1.0 apache-airflow-providers-ftp==2.0.1 apache-airflow-providers-google==4.0.0 apache-airflow-providers-http==2.0.1 apache-airflow-providers-imap==2.0.1 apache-airflow-providers-jira==2.0.1 apache-airflow-providers-postgres==2.4.0 apache-airflow-providers-redis==2.0.1 **apache-airflow-providers-sftp==2.4.0** apache-airflow-providers-slack==4.1.0 apache-airflow-providers-sqlite==2.0.1 **apache-airflow-providers-ssh==2.3.0** ### Apache Airflow version 2.2.3 (latest released) ### Operating System Ubuntu 20.04 ### Deployment Other Docker-based deployment ### Deployment details _No response_ ### What happened I believe this was introduced in this commit https://github.com/apache/airflow/commit/f35ad27080a2e1f29efc20a9bd0613af0f6ff2ec In File airflow/providers/sftp/hooks/sftp.py In lines 74-79 ```python def __init__( self, ssh_conn_id: Optional[str] = 'sftp_default', ftp_conn_id: Optional[str] = 'sftp_default', *args, **kwargs, ) -> None: if ftp_conn_id: warnings.warn( 'Parameter `ftp_conn_id` is deprecated.' 'Please use `ssh_conn_id` instead.', DeprecationWarning, stacklevel=2, ) kwargs['ssh_conn_id'] = ftp_conn_id self.ssh_conn_id = ssh_conn_id super().__init__(*args, **kwargs) ``` Since `ftp_conn_id` has a default value of `sftp_default`, it will always override `ssh_conn_id` unless explicitly set to None during init. ### What you expected to happen If you initialise the hook with `ssh_conn_id` parameter it should use that connection instead of ignoring the parameter and using the default value. The deprecation message is also triggered despite only passing in `ssh_conn_id` `<stdin>:1 DeprecationWarning: Parameter ftp_conn_id is deprecated.Please use ssh_conn_id instead.` ### How to reproduce ```python from airflow.providers.sftp.hooks.sftp import SFTPHook hook = SFTPHook(ssh_conn_id='some_custom_sftp_conn_id') ``` There will be a log message stating `[2022-01-07 01:12:05,234] {base.py:70} INFO - Using connection to: id: sftp_default.` ### Anything else This breaks the SFTPSensor and SFTPOperator ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20735

https://github.com/apache/airflow/pull/20756

9ea459a6bd8073f16dc197b1147f220293557dc8

c2fc760c9024ce9f0bec287679d9981f6ab1fd98

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/views.py", "tests/www/views/test_views_tasks.py"]

Edit Task Instance Page does not save updates.

### Apache Airflow version main (development) ### What happened On the Task Instances table, one can click on `Edit Record` and be directed to a `Edit Task Instance` page. There, if you try to change the state, it will simply not update nor are there any errors in the browser console. <img width="608" alt="Screen Shot 2022-01-04 at 10 10 52 AM" src="https://user-images.githubusercontent.com/4600967/148101472-fb6076fc-3fc3-44c4-a022-6625d5112551.png"> ### What you expected to happen Changes to state should actually update the task instance. ### How to reproduce Try to use the `Edit Task Instance` page ### Operating System Mac OSx ### Versions of Apache Airflow Providers _No response_ ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else This page is redundant, as a user can successfully edit task instance states from the table. Instead of wiring up the correct actions, I say we should just remove the `Edit Task Instance` page entirely. ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20655

https://github.com/apache/airflow/pull/30415

22bef613678e003dde9128ac05e6c45ce934a50c

b140c4473335e4e157ff2db85148dd120c0ed893

python

closed

apache/airflow

https://github.com/apache/airflow

["chart/templates/flower/flower-deployment.yaml", "chart/templates/pgbouncer/pgbouncer-deployment.yaml", "chart/templates/scheduler/scheduler-deployment.yaml", "chart/templates/statsd/statsd-deployment.yaml", "chart/templates/triggerer/triggerer-deployment.yaml", "chart/templates/webserver/webserver-deployment.yaml", "chart/templates/workers/worker-deployment.yaml", "chart/tests/test_airflow_common.py", "chart/values.schema.json", "chart/values.yaml"]

Add priorityClassName support

### Official Helm Chart version 1.2.0 ### Apache Airflow version 2.1.4 ### Kubernetes Version 1.21.2 ### Helm Chart configuration _No response_ ### Docker Image customisations _No response_ ### What happened _No response_ ### What you expected to happen _No response_ ### How to reproduce _No response_ ### Anything else It's currently impossible to assign a `priorityClassName` to the Airflow containers. Seems like a very useful feature to ensure that the Airflow infrastructure has higher priority than "regular" pods. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20632

https://github.com/apache/airflow/pull/20794

ab762a5a8ae147ae33500ee3c7e7a73d25d03ad7

ec41fd51e07ca2b9a66e0b99b730300c80a6d059

python

closed

apache/airflow

https://github.com/apache/airflow

["setup.cfg"]

Bump flask-appbuilder to >=3.3.4, <4.0.0

### Description Reason: Improper Authentication in Flask-AppBuilder: https://github.com/advisories/GHSA-m3rf-7m4w-r66q ### Use case/motivation _No response_ ### Related issues https://github.com/advisories/GHSA-m3rf-7m4w-r66q ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20627

https://github.com/apache/airflow/pull/20628

e1fbfc60d29af3fc0928b904ac80ca3b71a3a839

97261c642cbf07db91d252cf6b0b7ff184cd64c6

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api/common/experimental/mark_tasks.py"]

Triggers are not terminated when DAG is mark failed

### Apache Airflow version 2.2.3 (latest released) ### What happened Hello, I quite excited to new [Deferrable ("Async") Operators in AIP-40 ](https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=177050929), and going to adapt this by develop new async version of ExternalTaskSensor. But while doing, I find out that Deferrable ("Async") Operators are not canceled when DAG is mark failded/success.  When I mark DAG as failed, `wait_task` is canceled (changed to `failed` state), but `wait_task_async` still in `deferred` state and triggerer is keep poking.   ### What you expected to happen Deferrable ("Async") Operators should be canceled as sync version of operators ### How to reproduce Testing DAG. ```python from datetime import datetime, timedelta from airflow import DAG from airflow.operators.bash import BashOperator from airflow.sensors.external_task import ExternalTaskSensor, ExternalTaskSensorAsync with DAG( 'tutorial_async_sensor', default_args={ 'depends_on_past': False, 'email': ['airflow@example.com'], 'email_on_failure': False, 'email_on_retry': False, 'retries': 1, 'retry_delay': timedelta(minutes=5), }, description='A simple tutorial DAG using external sensor async', schedule_interval=timedelta(days=1), start_date=datetime(2021, 1, 1), catchup=False, tags=['example'], ) as dag: t1 = ExternalTaskSensorAsync( task_id='wait_task_async', external_dag_id="tutorial", external_task_id="sleep", execution_delta=timedelta(hours=1), poke_interval=5.0 ) t2 = ExternalTaskSensor( task_id='wait_task', external_dag_id="tutorial", external_task_id="sleep", execution_delta=timedelta(hours=1), poke_interval=5.0 ) t3 = BashOperator( task_id='echo', depends_on_past=False, bash_command='echo Hello world', retries=3, ) [t1, t2] >> t3 ``` for `ExternalTaskSensorAsync`, see #20583 ### Operating System Ubuntu 20.04 ### Versions of Apache Airflow Providers _No response_ ### Deployment Virtualenv installation ### Deployment details run `airflow standalone` ### Anything else _No response_ ### Are you willing to submit PR? - [x] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20580

https://github.com/apache/airflow/pull/20649

b83084b1b05415079972a76e3e535d40a1998de8

64c0bd50155dfdb84671ac35d645b812fafa78a1

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/commands/task_command.py", "tests/cli/commands/test_task_command.py"]

Airflow 2.2.3 : "airflow dags trigger" command gets "Calling `DAG.create_dagrun()` without an explicit data interval is deprecated"

### Apache Airflow version 2.2.3 (latest released) ### What happened When issuing the following command: `airflow dags trigger 'VMWARE_BACKUP' --conf '{"VM_NAME":"psfiplb1"}'` the system replays with: ``` /usr/local/lib/python3.8/dist-packages/airflow/api/common/experimental/trigger_dag.py:85 DeprecationWarning: Calling `DAG.create_dagrun()` without an explicit data interval is deprecated Created <DagRun VMWARE_BACKUP @ 2021-12-31T00:00:00+00:00: manual__2021-12-31T00:00:00+00:00, externally triggered: True> ``` ### What you expected to happen I have no other switch parameter to choose for avoiding the deprecation warning. My concern is about what will happen to the command when the deprecation will transforms itself in an error. ### How to reproduce _No response_ ### Operating System Ubuntu 20.04.3 LTS ### Versions of Apache Airflow Providers apache-airflow-providers-celery==2.1.0 apache-airflow-providers-ftp==2.0.1 apache-airflow-providers-http==2.0.1 apache-airflow-providers-imap==2.0.1 apache-airflow-providers-microsoft-mssql==2.0.1 apache-airflow-providers-microsoft-winrm==2.0.1 apache-airflow-providers-openfaas==2.0.0 apache-airflow-providers-oracle==2.0.1 apache-airflow-providers-samba==3.0.1 apache-airflow-providers-sftp==2.3.0 apache-airflow-providers-sqlite==2.0.1 apache-airflow-providers-ssh==2.3.0 ### Deployment Virtualenv installation ### Deployment details Airflow inside an LXD continer ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20579

https://github.com/apache/airflow/pull/27106

c3095d77b81c1a3a6510246b1fea61e6423d518b

70680ded7a4056882008b019f5d1a8f559a301cd

python

closed

apache/airflow

https://github.com/apache/airflow

["chart/templates/secrets/elasticsearch-secret.yaml", "chart/tests/test_elasticsearch_secret.py"]

Elasticsearch connection user and password should be optional

### Official Helm Chart version main (development) ### Apache Airflow version 2.2.3 (latest released) ### Kubernetes Version 1.21.5 ### Helm Chart configuration ```yaml elasticsearch: enabled: true connection: host: elasticsearch-master port: 9200 ``` ### Docker Image customisations _No response_ ### What happened Secret was created with this connection: ``` http://%3Cno+value%3E:%3Cno+value%3E@elasticsearch-master:9200 ``` ### What you expected to happen Secret created with this connection: ``` http://elasticsearch-master:9200 ``` ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20560

https://github.com/apache/airflow/pull/21222

5a6a2d604979cb70c5c9d3797738f0876dd38c3b

5dc6338346deca8e5a9a47df2da19f38eeac0ce8

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/exceptions.py", "airflow/models/dagbag.py", "airflow/models/param.py", "tests/dags/test_invalid_param.py", "tests/jobs/test_scheduler_job.py", "tests/models/test_dag.py", "tests/models/test_dagbag.py", "tests/models/test_param.py"]

Missing required parameter shows a stacktrace instead of a validation error

### Apache Airflow version 2.2.3 (latest released) ### What happened I wanted to improve the [Params concepts doc](https://airflow.apache.org/docs/apache-airflow/stable/concepts/params.html) to show how one can reference the params from a task. While doing so, I tried to run that DAG by clicking "Trigger" (i.e. I didn't opt to modify the params first). Then I saw this: ``` Oops. Something bad has happened. ... Python version: 3.9.9 Airflow version: 2.2.3+astro.1 Node: df9204fff6e6 ------------------------------------------------------------------------------- Traceback (most recent call last): File "/usr/local/lib/python3.9/site-packages/airflow/models/param.py", line 62, in __init__ jsonschema.validate(self.value, self.schema, format_checker=FormatChecker()) File "/usr/local/lib/python3.9/site-packages/jsonschema/validators.py", line 934, in validate raise error jsonschema.exceptions.ValidationError: 'NoValueSentinel' is too long Failed validating 'maxLength' in schema: {'maxLength': 4, 'minLength': 2, 'type': 'string'} On instance: 'NoValueSentinel' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/usr/local/lib/python3.9/site-packages/flask/app.py", line 2447, in wsgi_app response = self.full_dispatch_request() File "/usr/local/lib/python3.9/site-packages/flask/app.py", line 1952, in full_dispatch_request rv = self.handle_user_exception(e) File "/usr/local/lib/python3.9/site-packages/flask/app.py", line 1821, in handle_user_exception reraise(exc_type, exc_value, tb) File "/usr/local/lib/python3.9/site-packages/flask/_compat.py", line 39, in reraise raise value File "/usr/local/lib/python3.9/site-packages/flask/app.py", line 1950, in full_dispatch_request rv = self.dispatch_request() File "/usr/local/lib/python3.9/site-packages/flask/app.py", line 1936, in dispatch_request return self.view_functions[rule.endpoint](**req.view_args) File "/usr/local/lib/python3.9/site-packages/airflow/www/auth.py", line 51, in decorated return func(*args, **kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/www/decorators.py", line 72, in wrapper return f(*args, **kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/utils/session.py", line 70, in wrapper return func(*args, session=session, **kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/www/views.py", line 1650, in trigger dag = current_app.dag_bag.get_dag(dag_id) File "/usr/local/lib/python3.9/site-packages/airflow/utils/session.py", line 70, in wrapper return func(*args, session=session, **kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/models/dagbag.py", line 186, in get_dag self._add_dag_from_db(dag_id=dag_id, session=session) File "/usr/local/lib/python3.9/site-packages/airflow/models/dagbag.py", line 261, in _add_dag_from_db dag = row.dag File "/usr/local/lib/python3.9/site-packages/airflow/models/serialized_dag.py", line 180, in dag dag = SerializedDAG.from_dict(self.data) # type: Any File "/usr/local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 947, in from_dict return cls.deserialize_dag(serialized_obj['dag']) File "/usr/local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 861, in deserialize_dag v = {task["task_id"]: SerializedBaseOperator.deserialize_operator(task) for task in v} File "/usr/local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 861, in <dictcomp> v = {task["task_id"]: SerializedBaseOperator.deserialize_operator(task) for task in v} File "/usr/local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 641, in deserialize_operator v = cls._deserialize_params_dict(v) File "/usr/local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 459, in _deserialize_params_dict op_params[k] = cls._deserialize_param(v) File "/usr/local/lib/python3.9/site-packages/airflow/serialization/serialized_objects.py", line 439, in _deserialize_param return class_(**kwargs) File "/usr/local/lib/python3.9/site-packages/airflow/models/param.py", line 64, in __init__ raise ValueError(err) ValueError: 'NoValueSentinel' is too long Failed validating 'maxLength' in schema: {'maxLength': 4, 'minLength': 2, 'type': 'string'} On instance: 'NoValueSentinel' ``` I started with the dag in the docs, but ended up making some tweaks. Here's how it was when I saw the error. ``` from airflow import DAG from airflow.models.param import Param from airflow.operators.python import PythonOperator from datetime import datetime with DAG( "my_dag", start_date=datetime(1970, 1, 1), schedule_interval=None, params={ # a int param with default value "int_param": Param(10, type="integer", minimum=0, maximum=20), # a mandatory str param "str_param": Param(type="string", minLength=2, maxLength=4), # a param which can be None as well "dummy": Param(type=["null", "number", "string"]), # no data or type validations "old": "old_way_of_passing", # no data or type validations "simple": Param("im_just_like_old_param"), "email": Param( default="example@example.com", type="string", format="idn-email", minLength=5, maxLength=255, ), }, ) as the_dag: def print_these(*params): for param in params: print(param) PythonOperator( task_id="ref_params", python_callable=print_these, op_args=[ # you can modify them in jinja templates "{{ params.int_param + 10 }}", # or just leave them as-is "{{ params.str_param }}", "{{ params.dummy }}", "{{ params.old }}", "{{ params.simple }}", "{{ params.email }}", ], ) ``` ### What you expected to happen If there's a required parameter, and I try to trigger a dagrun, I should get a friendly warning explaining what I've done wrong. ### How to reproduce Run the dag above ### Operating System docker / debian ### Versions of Apache Airflow Providers n/a ### Deployment Astronomer ### Deployment details `astro dev start` Dockerfile: ``` FROM quay.io/astronomer/ap-airflow:2.2.3-onbuild ``` ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20559

https://github.com/apache/airflow/pull/20802

717169987e76f332d7ce92bc361d2fff6966f6f0

c59001d79facf7e472e0581ac8a538c25eebfda7

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/transfers/sftp_to_gcs.py", "tests/providers/google/cloud/transfers/test_sftp_to_gcs.py"]

gzip parameter of sftp_to_gcs operator is never passed to the GCS hook

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers List of versions is the one provided by Cloud Composer image `composer-1.17.7-airflow-2.1.4`. (cf. https://cloud.google.com/composer/docs/concepts/versioning/composer-versions#images ) Relevant here is `apache-airflow-providers-google==5.1.0` ### Apache Airflow version 2.1.4 ### Deployment Composer ### Deployment details _No response_ ### What happened Found on version 2.1.4 and reproduced locally on main branch (v2.2.3). When using `SFTPToGCSOperator` with `gzip=True`, no compression is actually performed, the files are copied/moved as-is to GCS. This happens because the `gzip` parameter isn't passed to the GCS Hook `upload()` call which then defaults to `False`. ### What you expected to happen I expect the files to be compressed when `gzip=True`. ### How to reproduce Create any `SFTPToGCSOperator` with `gzip=True` and upload a file. ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20552

https://github.com/apache/airflow/pull/20553

c5c18c54fa83463bc953249dc28edcbf7179da17

3a480f5ff41c2da4ae4fd6b2289e064ee42048a5

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow/templates-ref.rst"]

built in macros (macros.random, macros.time) need documentation change

### Apache Airflow version 2.2.3 (latest released) ### What happened My gut says that the way forward is to change the macros object so that it only exposes modules: - datetime - time - uuid - random ... and then leave it to the user to decide which functions on those modules they want to call. I'm not confident enough to make that change. If instead we want to change the docs to match the actual functionality, I can submit a PR for that. ### What you expected to happen When using either the bultin macros time or random they don't call datetime.time or random they instead call the builtin module time or for random returns a function instead of the module. ### How to reproduce ```python import datetime as dt import time from uuid import uuid4 from textwrap import dedent from airflow.models import DAG from airflow.operators.python import PythonOperator from dateutil.parser import parse as dateutil_parse """ According to the docs: macros.datetime - datetime.datetime macros.timedelta - datetime.timedelta macros.datetutil - dateutil package macros.time - datetime.time macros.uuid - python standard lib uuid macros.random - python standard lib random According to the code: macros.datetime - datetime.datetime macros.timedelta - datetime.timedelta macros.datetutil - dateutil package macros.time - python standard lib time <--- differs macros.uuid - python standard lib uuid macros.random - random.random <--- differs """ def date_time(datetime_obj): compare_obj = dt.datetime(2021, 12, 12, 8, 32, 23) assert datetime_obj == compare_obj def time_delta(timedelta_obj): compare_obj = dt.timedelta(days=3, hours=4) assert timedelta_obj == compare_obj def date_util(dateutil_obj): compare_obj = dateutil_parse("Thu Sep 26 10:36:28 2019") assert dateutil_obj == compare_obj def time_tester(time_obj): # note that datetime.time.time() gives an AttributeError # time.time() on the other hand, returns a float # this works because macro.time isn't 'datetime.time', like the docs say # it's just 'time' compare_obj = time.time() print(time_obj) print(compare_obj) # the macro might have captured a slightly differnt time than the task, # but they're not going to be more than 10s apart assert abs(time_obj - compare_obj) < 10 def uuid_tester(uuid_obj): compare_obj = uuid4() assert len(str(uuid_obj)) == len(str(compare_obj)) def random_tester(random_float): # note that 'random.random' is a function that returns a float # while 'random' is a module (and isn't callable) # the macro was 'macros.random()' and here we have a float: assert -0.1 < random_float < 100.1 # so the docs are wrong here too # macros.random actually returns a function, not the random module def show_docs(attr): print(attr.__doc__) with DAG( dag_id="builtin_macros_with_airflow_specials", schedule_interval=None, start_date=dt.datetime(1970, 1, 1), render_template_as_native_obj=True, # render templates using Jinja NativeEnvironment tags=["core"], ) as dag: test_functions = { "datetime": (date_time, "{{ macros.datetime(2021, 12, 12, 8, 32, 23) }}"), "timedelta": (time_delta, "{{ macros.timedelta(days=3, hours=4) }}"), "dateutil": ( date_util, "{{ macros.dateutil.parser.parse('Thu Sep 26 10:36:28 2019') }}", ), "time": (time_tester, "{{ macros.time.time() }}"), "uuid": (uuid_tester, "{{ macros.uuid.uuid4() }}"), "random": ( random_tester, "{{ 100 * macros.random() }}", ), } for name, (func, template) in test_functions.items(): ( PythonOperator( task_id=f"showdoc_{name}", python_callable=show_docs, op_args=[f"{{{{ macros.{name} }}}}"], ) >> PythonOperator( task_id=f"test_{name}", python_callable=func, op_args=[template] ) ) ``` ### Operating System Docker (debian:buster) ### Versions of Apache Airflow Providers 2.2.2, and 2.2.3 ### Deployment Other ### Deployment details Astro CLI with sequential executor ### Anything else Rather than changing the docs to describe what the code actually does would it be better to make the code behave in a way that is more consistent (e.g. toplevel modules only)? ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20545

https://github.com/apache/airflow/pull/20637

b1b8f304586e8ad181861dfe8ac15297c78f917b

8b2299b284ac15900f54bf8c84976cc01f4d597c

python

closed

apache/airflow

https://github.com/apache/airflow

[".pre-commit-config.yaml", "BREEZE.rst", "STATIC_CODE_CHECKS.rst", "breeze-complete", "chart/values.schema.json", "scripts/ci/pre_commit/pre_commit_chart_schema.py", "scripts/ci/pre_commit/pre_commit_vendor_k8s_json_schema.py"]

Offline chart installs broken

### Official Helm Chart version main (development) ### Apache Airflow version 2.2.3 (latest released) ### Kubernetes Version 1.21.5 ### Helm Chart configuration _No response_ ### Docker Image customisations _No response_ ### What happened If you don't have outbound routing to the internet, you cannot install/template/lint the helm chart, e.g: ``` $ helm upgrade --install oss ~/github/airflow/chart Release "oss" does not exist. Installing it now. Error: values don't meet the specifications of the schema(s) in the following chart(s): airflow: Get "https://raw.githubusercontent.com/yannh/kubernetes-json-schema/master/v1.22.0-standalone-strict/networkpolicypeer-networking-v1.json": dial tcp: lookup raw.githubusercontent.com: no such host ``` ### What you expected to happen The chart should function without requiring outbound internet access. ### How to reproduce Disable networking and try installing the chart. ### Anything else We use external schema references in our chart, e.g: https://github.com/apache/airflow/blob/8acfe8d82197448d2117beab29e688a68cec156a/chart/values.schema.json#L1298 Unfortunately, naively using relative filesystem reference in `values.schema.json` doesn't work properly in helm (see helm/helm#10481). ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20539

https://github.com/apache/airflow/pull/20544

f200bb1977655455f8acb79c9bd265df36f8ffce

afd84f6f2dcb81cb0c61e8b0563ff40ee75a2a6d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/views.py"]

is not bound to a Session; lazy load operation of attribute 'dag_model'

### Apache Airflow version 2.2.3 (latest released) ### What happened An error occurred while browsing the page (/rendered-k8s?dag_id=xxx&task_id=yyy&execution_date=zzz): ``` Parent instance <TaskInstance at 0x7fb0b01df940> is not bound to a Session; lazy load operation of attribute 'dag_model' cannot proceed (Background on this error at: http://sqlalche.me/e/13/bhk3) Traceback (most recent call last): args=self.command_as_list(), File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 527, in command_as_list dag = self.dag_model File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/orm/attributes.py", line 294, in __get__ return self.impl.get(instance_state(instance), dict_) File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/orm/attributes.py", line 730, in get value = self.callable_(state, passive) File "/home/airflow/.local/lib/python3.9/site-packages/sqlalchemy/orm/strategies.py", line 717, in _load_for_state raise orm_exc.DetachedInstanceError( sqlalchemy.orm.exc.DetachedInstanceError: Parent instance <TaskInstance at 0x7fb0b01df940> is not bound to a Session; lazy load operation of attribute 'dag_model' cannot proceed (Background on this error at: http://sqlalche.me/e/13/bhk3) ``` ### What you expected to happen _No response_ ### How to reproduce _No response_ ### Operating System NAME="CentOS Stream" VERSION="8" ### Versions of Apache Airflow Providers ``` apache-airflow-providers-cncf-kubernetes==2.2.0 apache-airflow-providers-elasticsearch==2.0.3 apache-airflow-providers-ftp==2.0.1 apache-airflow-providers-http==2.0.1 apache-airflow-providers-imap==2.0.1 apache-airflow-providers-mysql==2.1.1 apache-airflow-providers-postgres==2.3.0 apache-airflow-providers-sftp==2.1.1 apache-airflow-providers-sqlite==2.0.1 apache-airflow-providers-ssh==2.2.0 ``` ### Deployment Other 3rd-party Helm chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20534

https://github.com/apache/airflow/pull/21006

372849486cd455a4ff4821b01805a442f1a78417

a665f48b606065977e0d3952bc74635ce11726d1

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/commands/standalone_command.py"]

The standalone model bug, about port 8080

### Apache Airflow version 2.2.3 (latest released) ### What happened When I modify airflow.cfg about default webserver listening port to 8000, then start with "airflow standalone" A error will be printed: webserver | [2021-12-25 22:20:08 +0800] [16173] [ERROR] Connection in use: ('0.0.0.0', 8080) webserver | [2021-12-25 22:20:08 +0800] [16173] [ERROR] Retrying in 1 second. webserver | [2021-12-25 22:20:10 +0800] [16173] [ERROR] Connection in use: ('0.0.0.0', 8080) webserver | [2021-12-25 22:20:10 +0800] [16173] [ERROR] Retrying in 1 second. webserver | [2021-12-25 22:20:11 +0800] [16173] [ERROR] Connection in use: ('0.0.0.0', 8080) webserver | [2021-12-25 22:20:11 +0800] [16173] [ERROR] Retrying in 1 second. webserver | [2021-12-25 22:20:12 +0800] [16173] [ERROR] Connection in use: ('0.0.0.0', 8080) webserver | [2021-12-25 22:20:12 +0800] [16173] [ERROR] Retrying in 1 second. webserver | [2021-12-25 22:20:13 +0800] [16173] [ERROR] Connection in use: ('0.0.0.0', 8080) webserver | [2021-12-25 22:20:13 +0800] [16173] [ERROR] Retrying in 1 second. webserver | [2021-12-25 22:20:14 +0800] [16173] [ERROR] Can't connect to ('0.0.0.0', 8080) Source code position, https://github.com/apache/airflow/blob/main/airflow/cli/commands/standalone_command.py line 209, function is_ready return self.port_open(**8080**) and self.job_running(SchedulerJob) and self.job_running(TriggererJob) force the port to 8080, it's wrong. ### What you expected to happen _No response_ ### How to reproduce _No response_ ### Operating System All System ### Versions of Apache Airflow Providers _No response_ ### Deployment Other ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20496

https://github.com/apache/airflow/pull/20505

2976e6f829e727c01b9c2838e32d210d40e7a03c

f743e46c5a4fdd0b76fea2d07729b744644fc416

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/taskinstance.py", "tests/conftest.py", "tests/models/test_taskinstance.py"]

Custom Timetable error with CeleryExecutor

### Apache Airflow version 2.2.3 (latest released) ### What happened i'm really new to airflow, and i have an error when using Custom Timetable w/ CeleryExecutor ### What you expected to happen For the custom timetable to be implemented and used by DAG. error log as follows: ``` [2021-12-23 05:44:30,843: WARNING/ForkPoolWorker-2] Running <TaskInstance: example_cron_trivial_dag.dummy scheduled__2021-12-23T05:44:00+00:00 [queued]> on host 310bbd362d25 [2021-12-23 05:44:30,897: ERROR/ForkPoolWorker-2] Failed to execute task Not a valid timetable: <cron_trivial_timetable.CronTrivialTimetable object at 0x7fc15a0a4100>. Traceback (most recent call last): File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/executors/celery_executor.py", line 121, in _execute_in_fork args.func(args) File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/cli/cli_parser.py", line 48, in command return func(*args, **kwargs) File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/utils/cli.py", line 92, in wrapper return f(*args, **kwargs) File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/cli/commands/task_command.py", line 298, in task_run _run_task_by_selected_method(args, dag, ti) File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/cli/commands/task_command.py", line 105, in _run_task_by_selected_method _run_task_by_local_task_job(args, ti) File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/cli/commands/task_command.py", line 163, in _run_task_by_local_task_job run_job.run() File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/jobs/base_job.py", line 245, in run self._execute() File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/jobs/local_task_job.py", line 78, in _execute self.task_runner = get_task_runner(self) File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/task/task_runner/__init__.py", line 63, in get_task_runner task_runner = task_runner_class(local_task_job) File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/task/task_runner/standard_task_runner.py", line 35, in __init__ super().__init__(local_task_job) File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/task/task_runner/base_task_runner.py", line 48, in __init__ super().__init__(local_task_job.task_instance) File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/utils/log/logging_mixin.py", line 40, in __init__ self._set_context(context) File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/utils/log/logging_mixin.py", line 54, in _set_context set_context(self.log, context) File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/utils/log/logging_mixin.py", line 178, in set_context handler.set_context(value) File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/utils/log/file_task_handler.py", line 59, in set_context local_loc = self._init_file(ti) File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/utils/log/file_task_handler.py", line 264, in _init_file relative_path = self._render_filename(ti, ti.try_number) File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/utils/log/file_task_handler.py", line 80, in _render_filename context = ti.get_template_context() File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1912, in get_template_context 'next_ds': get_next_ds(), File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1868, in get_next_ds execution_date = get_next_execution_date() File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1862, in get_next_execution_date next_execution_date = dag.following_schedule(self.execution_date) File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/models/dag.py", line 595, in following_schedule data_interval = self.infer_automated_data_interval(timezone.coerce_datetime(dttm)) File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/models/dag.py", line 678, in infer_automated_data_interval raise ValueError(f"Not a valid timetable: {self.timetable!r}") ValueError: Not a valid timetable: <cron_trivial_timetable.CronTrivialTimetable object at 0x7fc15a0a4100> [2021-12-23 05:44:30,936: ERROR/ForkPoolWorker-2] Task airflow.executors.celery_executor.execute_command[7c76904d-1b61-4441-b5f0-96ef2ba7c3b7] raised unexpected: AirflowException('Celery command failed on host: 310bbd362d25') Traceback (most recent call last): File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/celery/app/trace.py", line 451, in trace_task R = retval = fun(*args, **kwargs) File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/celery/app/trace.py", line 734, in __protected_call__ return self.run(*args, **kwargs) File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/executors/celery_executor.py", line 90, in execute_command _execute_in_fork(command_to_exec, celery_task_id) File "/opt/rh/rh-python38/root/usr/local/lib/python3.8/site-packages/airflow/executors/celery_executor.py", line 101, in _execute_in_fork raise AirflowException('Celery command failed on host: ' + get_hostname()) airflow.exceptions.AirflowException: Celery command failed on host: 310bbd362d25 ``` seems like the same problem mentioned here, but by different reasons that i really don't understand https://github.com/apache/airflow/issues/19578#issuecomment-974654209 ### How to reproduce before 2.2.3 release, i'm having DAG import error w/ custom timetables, and since i only want Next Dag Run on UI be exact time, i will just change the code of CronDataIntervalTimetable by returning DataInterval.exact(end) in next_dagrun_info and infer_manual_data_interval. here's the new timetable plugin file that i created (simply copied and tweaked from CronDataIntervalTimetable): ``` import datetime from typing import Any, Dict, Optional, Union from croniter import CroniterBadCronError, CroniterBadDateError, croniter from dateutil.relativedelta import relativedelta from pendulum import DateTime from pendulum.tz.timezone import Timezone from airflow.plugins_manager import AirflowPlugin from airflow.compat.functools import cached_property from airflow.exceptions import AirflowTimetableInvalid from airflow.timetables.base import DagRunInfo, DataInterval, TimeRestriction, Timetable from airflow.utils.dates import cron_presets from airflow.utils.timezone import convert_to_utc, make_aware, make_naive Delta = Union[datetime.timedelta, relativedelta] class _TrivialTimetable(Timetable): """Basis for timetable implementations that schedule data intervals. This kind of timetable classes create periodic data intervals (exact times) from an underlying schedule representation (e.g. a cron expression, or a timedelta instance), and schedule a DagRun at the end of each interval (exact time). """ def _skip_to_latest(self, earliest: Optional[DateTime]) -> DateTime: """Bound the earliest time a run can be scheduled. This is called when ``catchup=False``. """ raise NotImplementedError() def _align(self, current: DateTime) -> DateTime: """Align given time to the scheduled. For fixed schedules (e.g. every midnight); this finds the next time that aligns to the declared time, if the given time does not align. If the schedule is not fixed (e.g. every hour), the given time is returned. """ raise NotImplementedError() def _get_next(self, current: DateTime) -> DateTime: """Get the first schedule after the current time.""" raise NotImplementedError() def _get_prev(self, current: DateTime) -> DateTime: """Get the last schedule before the current time.""" raise NotImplementedError() def next_dagrun_info( self, *, last_automated_data_interval: Optional[DataInterval], restriction: TimeRestriction, ) -> Optional[DagRunInfo]: earliest = restriction.earliest if not restriction.catchup: earliest = self._skip_to_latest(earliest) if last_automated_data_interval is None: # First run; schedule the run at the first available time matching # the schedule, and retrospectively create a data interval for it. if earliest is None: return None start = self._align(earliest) else: # There's a previous run. Create a data interval starting from when # the end of the previous interval. start = last_automated_data_interval.end if restriction.latest is not None and start > restriction.latest: return None end = self._get_next(start) return DagRunInfo.exact(end) def _is_schedule_fixed(expression: str) -> bool: """Figures out if the schedule has a fixed time (e.g. 3 AM every day). :return: True if the schedule has a fixed time, False if not. Detection is done by "peeking" the next two cron trigger time; if the two times have the same minute and hour value, the schedule is fixed, and we *don't* need to perform the DST fix. This assumes DST happens on whole minute changes (e.g. 12:59 -> 12:00). """ cron = croniter(expression) next_a = cron.get_next(datetime.datetime) next_b = cron.get_next(datetime.datetime) return next_b.minute == next_a.minute and next_b.hour == next_a.hour class CronTrivialTimetable(_TrivialTimetable): """Timetable that schedules data intervals with a cron expression. This corresponds to ``schedule_interval=<cron>``, where ``<cron>`` is either a five/six-segment representation, or one of ``cron_presets``. The implementation extends on croniter to add timezone awareness. This is because crontier works only with naive timestamps, and cannot consider DST when determining the next/previous time. Don't pass ``@once`` in here; use ``OnceTimetable`` instead. """ def __init__(self, cron: str, timezone: Timezone) -> None: self._expression = cron_presets.get(cron, cron) self._timezone = timezone @classmethod def deserialize(cls, data: Dict[str, Any]) -> "Timetable": from airflow.serialization.serialized_objects import decode_timezone return cls(data["expression"], decode_timezone(data["timezone"])) def __eq__(self, other: Any) -> bool: """Both expression and timezone should match. This is only for testing purposes and should not be relied on otherwise. """ if not isinstance(other, CronTrivialTimetable): return NotImplemented return self._expression == other._expression and self._timezone == other._timezone @property def summary(self) -> str: return self._expression def serialize(self) -> Dict[str, Any]: from airflow.serialization.serialized_objects import encode_timezone return {"expression": self._expression, "timezone": encode_timezone(self._timezone)} def validate(self) -> None: try: croniter(self._expression) except (CroniterBadCronError, CroniterBadDateError) as e: raise AirflowTimetableInvalid(str(e)) @cached_property def _should_fix_dst(self) -> bool: # This is lazy so instantiating a schedule does not immediately raise # an exception. Validity is checked with validate() during DAG-bagging. return not _is_schedule_fixed(self._expression) def _get_next(self, current: DateTime) -> DateTime: """Get the first schedule after specified time, with DST fixed.""" naive = make_naive(current, self._timezone) cron = croniter(self._expression, start_time=naive) scheduled = cron.get_next(datetime.datetime) if not self._should_fix_dst: return convert_to_utc(make_aware(scheduled, self._timezone)) delta = scheduled - naive return convert_to_utc(current.in_timezone(self._timezone) + delta) def _get_prev(self, current: DateTime) -> DateTime: """Get the first schedule before specified time, with DST fixed.""" naive = make_naive(current, self._timezone) cron = croniter(self._expression, start_time=naive) scheduled = cron.get_prev(datetime.datetime) if not self._should_fix_dst: return convert_to_utc(make_aware(scheduled, self._timezone)) delta = naive - scheduled return convert_to_utc(current.in_timezone(self._timezone) - delta) def _align(self, current: DateTime) -> DateTime: """Get the next scheduled time. This is ``current + interval``, unless ``current`` is first interval, then ``current`` is returned. """ next_time = self._get_next(current) if self._get_prev(next_time) != current: return next_time return current def _skip_to_latest(self, earliest: Optional[DateTime]) -> DateTime: """Bound the earliest time a run can be scheduled. The logic is that we move start_date up until one period before, so the current time is AFTER the period end, and the job can be created... """ current_time = DateTime.utcnow() next_start = self._get_next(current_time) last_start = self._get_prev(current_time) if next_start == current_time: new_start = last_start elif next_start > current_time: new_start = self._get_prev(last_start) else: raise AssertionError("next schedule shouldn't be earlier") if earliest is None: return new_start return max(new_start, earliest) def infer_manual_data_interval(self, *, run_after: DateTime) -> DataInterval: # Get the last complete period before run_after, e.g. if a DAG run is # scheduled at each midnight, the data interval of a manually triggered # run at 1am 25th is between 0am 24th and 0am 25th. end = self._get_prev(self._align(run_after)) return DataInterval.exact(end) class CronTrivialTimetablePlugin(AirflowPlugin): name = "cron_trivial_timetable_plugin" timetables = [CronTrivialTimetable] ``` the dag file i'm using/testing: ``` import pendulum from datetime import datetime, timedelta from typing import Dict from airflow.decorators import task from airflow.models import DAG from airflow.operators.bash import BashOperator from airflow.operators.dummy import DummyOperator from cron_trivial_timetable import CronTrivialTimetable with DAG( dag_id="example_cron_trivial_dag", start_date=datetime(2021,11,14,12,0,tzinfo=pendulum.timezone('Asia/Tokyo')), max_active_runs=1, timetable=CronTrivialTimetable('*/2 * * * *', pendulum.timezone('Asia/Tokyo')), default_args={ 'owner': '********', 'depends_on_past': False, 'email': [**************], 'email_on_failure': False, 'email_on_retry': False, 'retries': 0, 'retry_delay': timedelta(seconds=1), 'end_date': datetime(2101, 1, 1), }, tags=['testing'], catchup=False ) as dag: dummy = BashOperator(task_id='dummy', queue='daybatch', bash_command="date") ``` ### Operating System CentOS-7 ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details my scheduler & workers are in different dockers, hence i'm using CeleryExecutor ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20475

https://github.com/apache/airflow/pull/20486

8acfe8d82197448d2117beab29e688a68cec156a

9e315ff7caec7fd3d4c0dfe8b89ee2a1c7b5fe3a

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/dag.py", "airflow/models/dagrun.py", "tests/models/test_dag.py", "tests/models/test_dagrun.py", "tests/models/test_taskinstance.py"]

no logs returned when an end_date specified at the task level is after dag's end date

### Apache Airflow version 2.2.2 ### What happened When you set an 'end_date' that is after the date for the dag parameter 'end_date' the task fails and no logs are given to the user. ### What you expected to happen I expected the task to succeed with an 'end_date' of the date specified at the task level. ### How to reproduce ``` from airflow.models import DAG from airflow.operators.python import PythonOperator from datetime import datetime def this_passes(): pass with DAG( dag_id="end_date", schedule_interval=None, start_date=datetime(2021, 1, 1), end_date=datetime(2021, 1, 2), tags=["dagparams"], ) as dag: t1 = PythonOperator( task_id="passer", python_callable=this_passes, end_date=datetime(2021, 2, 1), ) ``` ### Operating System Docker (debian:buster) ### Versions of Apache Airflow Providers _No response_ ### Deployment Other Docker-based deployment ### Deployment details Using the astro cli with docker image: quay.io/astronomer/ap-airflow:2.2.2-buster-onbuild ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20471

https://github.com/apache/airflow/pull/20920

f612a2f56add4751e959625c49368d09a2a47d55

85871eba420f3324432f55f74fe57005ff47a21c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/hooks/base_aws.py", "airflow/providers/amazon/aws/hooks/glue.py", "airflow/providers/amazon/aws/hooks/s3.py"]

Release 2.5.1 of Amazon Provider

### Apache Airflow version 2.2.3 (latest released) ### What happened The 2.5.0 version of Amazon Provider contains breaking changes - https://airflow.apache.org/docs/apache-airflow-providers-amazon/stable/commits.html specifically https://github.com/apache/airflow/commit/83b51e53062dc596a630edd4bd01407a556f1aa6 or the combination of the following: - https://github.com/apache/airflow/commit/83b51e53062dc596a630edd4bd01407a556f1aa6 - https://github.com/apache/airflow/commit/d58df468c8d77c5d45e80f2333eb074bb7771a95 - https://github.com/apache/airflow/commit/4be04143a5f7e246127e942bf1d73abcd22ce189 and confirmed by @uranusjr I have yanked 2.5.0 and we will need to release ~3.0.0~ 2.5.1 with backwards compatibility fixes I have updated the constraints for 2.2.3 for now - https://github.com/apache/airflow/commit/62d490d4da17e35d4ddcd4ee38902a8a4e9bbfff UPDATED: (@potiuk) to reflect that we are going to release 2.5.1 instead of 3.0.0

https://github.com/apache/airflow/issues/20457

https://github.com/apache/airflow/pull/20463

81f92d6c321992905d239bb9e8556720218fe745

2ab2ae8849bf6d80a700b1b74cef37eb187161ad

python

closed

apache/airflow

https://github.com/apache/airflow

["tests/providers/google/cloud/hooks/test_cloud_memorystore.py", "tests/providers/google/cloud/hooks/test_looker.py", "tests/providers/google/cloud/transfers/test_calendar_to_gcs.py"]

The "has_calls" is used in place of "assert_has_calls" in a few places

### Body As explained in https://github.com/apache/airflow/pull/20428#issuecomment-998714275 we seem to have number (not big) of tests that use "has_calls" rather than "assert_has_calls". :scream: :scream: :scream: :scream: :scream: :scream: :scream: :scream: :scream: What "has_calls" does is acually calling "has_calls" method on the mock :) . Which make them no-assertion tests: :scream: :scream: :scream: :scream: :scream: :scream: :scream: :scream: :scream: The list of those tests: - [x] ./tests/providers/google/common/hooks/test_base_google.py: mock_check_output.has_calls( - [x] ./tests/providers/google/common/hooks/test_base_google.py: mock_check_output.has_calls( - [x] ./tests/providers/google/cloud/transfers/test_sheets_to_gcs.py: mock_sheet_hook.return_value.get_values.has_calls(calls) - [x] ./tests/providers/google/cloud/transfers/test_sheets_to_gcs.py: mock_upload_data.has_calls(calls) - [x] ./tests/providers/google/cloud/hooks/test_bigquery.py: mock_poll_job_complete.has_calls(mock.call(running_job_id), mock.call(running_job_id)) - [x] ./tests/providers/google/cloud/hooks/test_bigquery.py: mock_schema.has_calls([mock.call(x, "") for x in ["field_1", "field_2"]]) - [x] ./tests/providers/google/cloud/hooks/test_bigquery.py: assert mock_insert.has_calls( - [x] ./tests/providers/google/cloud/hooks/test_pubsub.py: publish_method.has_calls(calls) - [x] ./tests/providers/google/cloud/hooks/test_cloud_memorystore.py: mock_get_conn.return_value.get_instance.has_calls( - [x] ./tests/providers/google/cloud/hooks/test_cloud_memorystore.py: mock_get_conn.return_value.get_instance.has_calls( - [x] ./tests/providers/google/cloud/hooks/test_cloud_memorystore.py: mock_get_conn.return_value.get_instance.has_calls( - [x] ./tests/providers/google/cloud/hooks/test_dataproc.py: mock_get_job.has_calls(calls) - [x] ./tests/providers/google/cloud/hooks/test_dataproc.py: mock_get_job.has_calls(calls) - [x] ./tests/providers/google/suite/operators/test_sheets.py: mock_xcom.has_calls(calls) - [x] ./tests/providers/http/operators/test_http.py: mock_info.has_calls(calls) - [ ] ./tests/providers/airbyte/hooks/test_airbyte.py: assert mock_get_job.has_calls(calls) - [ ] ./tests/providers/airbyte/hooks/test_airbyte.py: assert mock_get_job.has_calls(calls) - [ ] ./tests/providers/airbyte/hooks/test_airbyte.py: assert mock_get_job.has_calls(calls) - [ ] ./tests/providers/airbyte/hooks/test_airbyte.py: assert mock_get_job.has_calls(calls) - [ ] ./tests/providers/airbyte/hooks/test_airbyte.py: assert mock_get_job.has_calls(calls) We should fix those tests and likelly add pre-commit to ban this. Thanks to @jobegrabber for noticing it! ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/20453

https://github.com/apache/airflow/pull/23001

8e75e2349791ee606203d5ba9035146e8a3be3dc

3a2eb961ca628d962ed5fad68760ef3439b2f40b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/exceptions.py", "airflow/models/dagbag.py", "airflow/models/param.py", "tests/dags/test_invalid_param.py", "tests/jobs/test_scheduler_job.py", "tests/models/test_dag.py", "tests/models/test_dagbag.py", "tests/models/test_param.py"]

NoValueSentinel in Param is incorrectly serialised

### Apache Airflow version 2.2.2 (latest released) ### What happened Hi We upgraded to version 2.2.2 and starting getting errors in the UI when we have DAG params that are numeric (tried with types 'number', 'integer' and 'float' and got the same error. ``` Python version: 3.7.12 Airflow version: 2.2.2 Node: airflow2-webserver-6df5fc45bd-5vdhs ------------------------------------------------------------------------------- Traceback (most recent call last): File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/param.py", line 61, in __init__ jsonschema.validate(self.value, self.schema, format_checker=FormatChecker()) File "/home/airflow/.local/lib/python3.7/site-packages/jsonschema/validators.py", line 934, in validate raise error jsonschema.exceptions.ValidationError: 'NoValueSentinel' is not of type 'number' Failed validating 'type' in schema: {'type': 'number'} On instance: 'NoValueSentinel' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/airflow/.local/lib/python3.7/site-packages/flask/app.py", line 2447, in wsgi_app response = self.full_dispatch_request() File "/home/airflow/.local/lib/python3.7/site-packages/flask/app.py", line 1952, in full_dispatch_request rv = self.handle_user_exception(e) File "/home/airflow/.local/lib/python3.7/site-packages/flask/app.py", line 1821, in handle_user_exception reraise(exc_type, exc_value, tb) File "/home/airflow/.local/lib/python3.7/site-packages/flask/_compat.py", line 39, in reraise raise value File "/home/airflow/.local/lib/python3.7/site-packages/flask/app.py", line 1950, in full_dispatch_request rv = self.dispatch_request() File "/home/airflow/.local/lib/python3.7/site-packages/flask/app.py", line 1936, in dispatch_request return self.view_functions[rule.endpoint](**req.view_args) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/www/auth.py", line 51, in decorated return func(*args, **kwargs) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/www/decorators.py", line 109, in view_func return f(*args, **kwargs) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/www/decorators.py", line 72, in wrapper return f(*args, **kwargs) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/utils/session.py", line 70, in wrapper return func(*args, session=session, **kwargs) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/www/views.py", line 2426, in graph dag = current_app.dag_bag.get_dag(dag_id) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/utils/session.py", line 70, in wrapper return func(*args, session=session, **kwargs) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/dagbag.py", line 186, in get_dag self._add_dag_from_db(dag_id=dag_id, session=session) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/dagbag.py", line 261, in _add_dag_from_db dag = row.dag File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/serialized_dag.py", line 180, in dag dag = SerializedDAG.from_dict(self.data) # type: Any File "/home/airflow/.local/lib/python3.7/site-packages/airflow/serialization/serialized_objects.py", line 947, in from_dict return cls.deserialize_dag(serialized_obj['dag']) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/serialization/serialized_objects.py", line 861, in deserialize_dag v = {task["task_id"]: SerializedBaseOperator.deserialize_operator(task) for task in v} File "/home/airflow/.local/lib/python3.7/site-packages/airflow/serialization/serialized_objects.py", line 861, in <dictcomp> v = {task["task_id"]: SerializedBaseOperator.deserialize_operator(task) for task in v} File "/home/airflow/.local/lib/python3.7/site-packages/airflow/serialization/serialized_objects.py", line 641, in deserialize_operator v = cls._deserialize_params_dict(v) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/serialization/serialized_objects.py", line 459, in _deserialize_params_dict op_params[k] = cls._deserialize_param(v) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/serialization/serialized_objects.py", line 439, in _deserialize_param return class_(**kwargs) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/param.py", line 63, in __init__ raise ValueError(err) ValueError: 'NoValueSentinel' is not of type 'number' Failed validating 'type' in schema: {'type': 'number'} On instance: 'NoValueSentinel'` ``` ### What you expected to happen shouldn't cause a serialization error. ### How to reproduce This is a small DAG to reproduce this error. We use airflow on kubernetes with: Python version: 3.7.12 Airflow version: 2.2.2 ``` from airflow import DAG from airflow.models.param import Param from airflow.operators.dummy import DummyOperator from datetime import datetime params = { 'wd': Param(type='number', description="demo numeric param") } default_args ={ 'depends_on_past': False, 'start_date': datetime(2021, 6, 16) } with DAG ('mydag',params=params,default_args=default_args,catchup=False,schedule_interval=None) as dag: t1 = DummyOperator(task_id='task1') ``` ### Operating System centos ### Versions of Apache Airflow Providers _No response_ ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### Anything else On every dag with a numeric type of param ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20442

https://github.com/apache/airflow/pull/20802

717169987e76f332d7ce92bc361d2fff6966f6f0

c59001d79facf7e472e0581ac8a538c25eebfda7

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/transfers/bigquery_to_gcs.py", "airflow/providers/google/cloud/transfers/bigquery_to_mssql.py", "airflow/providers/google/cloud/transfers/bigquery_to_mysql.py"]

Deprecated arg is passed to BigQueryHook

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers `apache-airflow-providers-google==1.0.0` ### Apache Airflow version 2.0.0 ### Operating System Debian GNU/Linux 10 (buster) ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### What happened Within the `execute` method of `BigQueryToGCSOperator`, a deprecated arg – namely `bigquery_conn_id` – is being passed to `BigQueryHook`'s init method and causing a warning. **Relevant code**: https://github.com/apache/airflow/blob/85bedd03c33a1d6c6339e846558727bf20bc16f7/airflow/providers/google/cloud/transfers/bigquery_to_gcs.py#L135 **Warning**: ``` WARNING - /home/airflow/.local/lib/python3.7/site-packages/airflow/providers/google/cloud/transfers/bigquery_to_gcs.py:140 DeprecationWarning: The bigquery_conn_id parameter has been deprecated. You should pass the gcp_conn_id parameter. ``` ### What you expected to happen No `DeprecationWarning` should occur ### How to reproduce Call the execute method of `BigQueryToGCSOperator` ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20437

https://github.com/apache/airflow/pull/20502

b5d520cf73100df714c71ac9898a97bc0df29a31

7d4d38b546c44287f8a9d09c4fc141cbea736511

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/tableau/operators/tableau.py", "tests/providers/tableau/operators/test_tableau.py"]

Tableau provider signs out prior to blocking refresh check

### Apache Airflow version 2.2.2 (latest released) ### What happened It logs into the Tableau server, starts a refresh, and then throws a NotSignedInError exception: ``` [2021-12-20, 07:40:13 CST] {{base.py:70}} INFO - Using connection to: id: tableau. Host: https://xxx/, Port: None, Schema: , Login: xxx, Password: ***, extra: {} [2021-12-20, 07:40:14 CST] {{auth_endpoint.py:37}} INFO - Signed into https://xxx/ as user with id xxx-xxx-xxx-xxx-xxx [2021-12-20, 07:40:14 CST] {{workbooks_endpoint.py:41}} INFO - Querying all workbooks on site [2021-12-20, 07:40:14 CST] {{tableau.py:141}} INFO - Found matching with id xxx-xxx-xxx-xxx-xxx [2021-12-20, 07:40:14 CST] {{auth_endpoint.py:53}} INFO - Signed out [2021-12-20, 07:40:14 CST] {{jobs_endpoint.py:41}} INFO - Query for information about job xxx-xxx-xxx-xxx-xxx [2021-12-20, 07:40:14 CST] {{taskinstance.py:1703}} ERROR - Task failed with exception Traceback (most recent call last): File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1332, in _run_raw_task self._execute_task_with_callbacks(context) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1458, in _execute_task_with_callbacks result = self._execute_task(context, self.task) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1509, in _execute_task result = execute_callable(context=context) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/tableau/operators/tableau.py", line 124, in execute if not tableau_hook.wait_for_state( File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/tableau/hooks/tableau.py", line 180, in wait_for_state finish_code = self.get_job_status(job_id=job_id) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/tableau/hooks/tableau.py", line 163, in get_job_status return TableauJobFinishCode(int(self.server.jobs.get_by_id(job_id).finish_code)) File "/home/airflow/.local/lib/python3.9/site-packages/tableauserverclient/server/endpoint/endpoint.py", line 177, in wrapper return func(self, *args, **kwargs) File "/home/airflow/.local/lib/python3.9/site-packages/tableauserverclient/server/endpoint/jobs_endpoint.py", line 42, in get_by_id url = "{0}/{1}".format(self.baseurl, job_id) File "/home/airflow/.local/lib/python3.9/site-packages/tableauserverclient/server/endpoint/jobs_endpoint.py", line 14, in baseurl return "{0}/sites/{1}/jobs".format(self.parent_srv.baseurl, self.parent_srv.site_id) File "/home/airflow/.local/lib/python3.9/site-packages/tableauserverclient/server/server.py", line 168, in site_id raise NotSignedInError(error) tableauserverclient.server.exceptions.NotSignedInError: Missing site ID. You must sign in first. [2021-12-20, 07:40:14 CST] {{taskinstance.py:1270}} INFO - Marking task as FAILED. dag_id=tableau-refresh, task_id=refresh_tableau_workbook, execution_date=20211220T194008, start_date=20211220T194013, end_date=20211220T194014 ``` ### What you expected to happen It should've logged into Tableau, started a refresh, and then waited for the refresh to finish. ### How to reproduce Use a TableauOperator with a blocking refresh: ``` TableauOperator( resource='workbooks', method='refresh', find='Tableau workbook', match_with='name', site_id='tableau_site', task_id='refresh_tableau_workbook', tableau_conn_id='tableau', blocking_refresh=True, dag=dag, ) ``` ### Operating System Debian GNU/Linux 10 (buster) ### Versions of Apache Airflow Providers apache-airflow-providers-tableau==2.1.2 ### Deployment Docker-Compose ### Deployment details I'm using the official `apache/airflow:2.2.2-python3.9` Docker image ### Anything else It occurs every time I use the new TableauOperator to do a blocking refresh of a workbook. ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20432

https://github.com/apache/airflow/pull/20433

20d863007be02beccf598cf15909b7caf40f8019

636ae0a33dff63f899bc554e6585104776398bef

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/common/hooks/base_google.py", "tests/providers/google/common/hooks/test_base_google.py"]

GKE Authentication not possible with user ADC and project ID set in either connection or `gcloud` config

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers `apache-airflow-providers-google==6.1.0` ### Apache Airflow version 2.1.4 ### Operating System Debian GNU/Linux 11 (bullseye) ### Deployment Docker-Compose ### Deployment details At my company we're developing our Airflow DAGs in local environments based on Docker Compose. To authenticate against the GCP, we don't use service accounts and their keys, but instead use our user credentials and set them up as Application Default Credentials (ADC), i.e. we run ``` $ gcloud auth login $ gcloud gcloud auth application-default login ``` We also set the default the Project ID in both `gcloud` and Airflow connections, i.e. ``` $ gcloud config set project $PROJECT $ # run the following inside the Airflow Docker container $ airflow connections delete google_cloud_default $ airflow connections add google_cloud_default \ --conn-type=google_cloud_platform \ --conn-extra='{"extra__google_cloud_platform__project":"$PROJECT"}' ``` ### What happened It seems that due to [this part](https://github.com/apache/airflow/blob/ed604b6/airflow/providers/google/common/hooks/base_google.py#L518..L541) in `base_google.py`, when the Project ID is set in either the Airflow connections or `gcloud` config, `gcloud auth` (specifically `gcloud auth activate-refresh-token`) will not be executed. This results in e.g. `gcloud container clusters get-credentials` in the `GKEStartPodOperator` to fail, since `You do not currently have an active account selected`: ``` [2021-12-20 15:21:12,059] {credentials_provider.py:295} INFO - Getting connection using `google.auth.default()` since no key file is defined for hook. [2021-12-20 15:21:12,073] {logging_mixin.py:109} WARNING - /usr/local/lib/python3.8/site-packages/google/auth/_default.py:70 UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a "quota exceeded" or "API not enabled" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/ [2021-12-20 15:21:13,863] {process_utils.py:135} INFO - Executing cmd: gcloud container clusters get-credentials REDACTED --zone europe-west1-b --project REDACTED [2021-12-20 15:21:13,875] {process_utils.py:139} INFO - Output: [2021-12-20 15:21:14,522] {process_utils.py:143} INFO - ERROR: (gcloud.container.clusters.get-credentials) You do not currently have an active account selected. [2021-12-20 15:21:14,522] {process_utils.py:143} INFO - Please run: [2021-12-20 15:21:14,523] {process_utils.py:143} INFO - [2021-12-20 15:21:14,523] {process_utils.py:143} INFO - $ gcloud auth login [2021-12-20 15:21:14,523] {process_utils.py:143} INFO - [2021-12-20 15:21:14,523] {process_utils.py:143} INFO - to obtain new credentials. [2021-12-20 15:21:14,523] {process_utils.py:143} INFO - [2021-12-20 15:21:14,523] {process_utils.py:143} INFO - If you have already logged in with a different account: [2021-12-20 15:21:14,523] {process_utils.py:143} INFO - [2021-12-20 15:21:14,523] {process_utils.py:143} INFO - $ gcloud config set account ACCOUNT [2021-12-20 15:21:14,523] {process_utils.py:143} INFO - [2021-12-20 15:21:14,523] {process_utils.py:143} INFO - to select an already authenticated account to use. [2021-12-20 15:21:14,618] {taskinstance.py:1463} ERROR - Task failed with exception Traceback (most recent call last): File "/usr/local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1165, in _run_raw_task self._prepare_and_execute_task_with_callbacks(context, task) File "/usr/local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1283, in _prepare_and_execute_task_with_callbacks result = self._execute_task(context, task_copy) File "/usr/local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1313, in _execute_task result = task_copy.execute(context=context) File "/usr/local/lib/python3.8/site-packages/airflow/providers/google/cloud/operators/kubernetes_engine.py", line 355, in execute execute_in_subprocess(cmd) File "/usr/local/lib/python3.8/site-packages/airflow/utils/process_utils.py", line 147, in execute_in_subprocess raise subprocess.CalledProcessError(exit_code, cmd) subprocess.CalledProcessError: Command '['gcloud', 'container', 'clusters', 'get-credentials', 'REDACTED', '--zone', 'europe-west1-b', '--project', 'REDACTED']' returned non-zero exit status 1. ``` If we set the environment variable `GOOGLE_APPLICATION_CREDENTIALS`, `gcloud auth activate-service-account` is run which only works with proper service account credentials, not user credentials. ### What you expected to happen From my POV, it should work to 1. have the Project ID set in the `gcloud` config and/or Airflow variables and still be able to use user credentials with GCP Operators, 2. set `GOOGLE_APPLICATION_CREDENTIALS` to a file containing user credentials and be able to use these credentials with GCP Operators. Item 1 was definitely possible in Airflow 1. ### How to reproduce See Deployment Details. In essence: - Run Airflow within Docker Compose (but it's not only Docker Compose that is affected, as far as I can see). - Use user credentials with `gcloud`; `gcloud auth login`, `gcloud auth application-default login` - Configure project ID in `gcloud` config (mounted in the Docker container) and/or Airflow connection - Run `GKEStartOperator` ### Anything else Currently, the only workaround (apart from using service accounts) seems to be to not set a default project in either the `gcloud` config or `google_cloud_platform` connections. ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20426

https://github.com/apache/airflow/pull/20428

83f8e178ba7a3d4ca012c831a5bfc2cade9e812d

4233ebe5cea4862dbf16c9d7c72c4fdd11db9774

python

closed

apache/airflow

https://github.com/apache/airflow

["docs/apache-airflow/howto/email-config.rst", "docs/apache-airflow/img/email_connection.png"]

Need clearer documentation on setting up Sendgrid to send emails

### Describe the issue with documentation https://airflow.apache.org/docs/apache-airflow/stable/howto/email-config.html needs to have more documentation on how to use sendgrid to send emails. Like would installing the provider package guarantee the appearance of the "Email" connection type in the frontend? I do not think we can find a Email connection. Secondly do we need to import the providers packages in our dags to make it work or options like email_on_success etc should be automatically working once this is set up. ### How to solve the problem Better documentation ### Anything else Better documentation. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20419

https://github.com/apache/airflow/pull/21958

cc4b05654e9c8b6d1b3185c5690da87a29b66a4b

c9297808579c0d4f93acfd6791172193be19721b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/cli/commands/task_command.py"]

DagRun for <FOO> with run_id or execution_date of 'manual__XXXXX' not found

### Apache Airflow version 2.2.2 (latest released) ### What happened After upgrading from Airflow 2.1.4 to 2.2.2, every DAG gives this error upon execution: > [2021-12-17, 15:01:12 UTC] {taskinstance.py:1259} INFO - Executing <Task(_PythonDecoratedOperator): print_the_context> on 2021-12-17 15:01:08.943254+00:00 [2021-12-17, 15:01:12 UTC] {standard_task_runner.py:52} INFO - Started process 873 to run task [2021-12-17, 15:01:12 UTC] {standard_task_runner.py:76} INFO - Running: ['airflow', 'tasks', 'run', 'example_python_operator', 'print_the_context', 'manual__2021-12-17T15:01:08.943254+00:00', '--job-id', '326', '--raw', '--subdir', 'DAGS_FOLDER/test.py', '--cfg-path', '/tmp/tmpej1imvkr', '--error-file', '/tmp/tmpqn9ad7em'] [2021-12-17, 15:01:12 UTC] {standard_task_runner.py:77} INFO - Job 326: Subtask print_the_context [2021-12-17, 15:01:12 UTC] {standard_task_runner.py:92} ERROR - Failed to execute job 326 for task print_the_context Traceback (most recent call last): File "/home/ec2-user/venv/lib/python3.7/site-packages/airflow/task/task_runner/standard_task_runner.py", line 85, in _start_by_fork args.func(args, dag=self.dag) File "/home/ec2-user/venv/lib/python3.7/site-packages/airflow/cli/cli_parser.py", line 48, in command return func(*args, **kwargs) File "/home/ec2-user/venv/lib/python3.7/site-packages/airflow/utils/cli.py", line 92, in wrapper return f(*args, **kwargs) File "/home/ec2-user/venv/lib/python3.7/site-packages/airflow/cli/commands/task_command.py", line 287, in task_run ti = _get_ti(task, args.execution_date_or_run_id) File "/home/ec2-user/venv/lib/python3.7/site-packages/airflow/utils/session.py", line 70, in wrapper return func(*args, session=session, **kwargs) File "/home/ec2-user/venv/lib/python3.7/site-packages/airflow/cli/commands/task_command.py", line 86, in _get_ti dag_run = _get_dag_run(task.dag, exec_date_or_run_id, create_if_necssary, session) File "/home/ec2-user/venv/lib/python3.7/site-packages/airflow/cli/commands/task_command.py", line 80, in _get_dag_run ) from None airflow.exceptions.DagRunNotFound: DagRun for example_python_operator with run_id or execution_date of 'manual__2021-12-17T15:01:08.943254+00:00' not found Both tables `airflowdb.task_instance` and `airflowdb.dag_run` have rows with `run_id` equal to "manual__2021-12-17T15:01:08.943254+00:00". The issue seems to arise in the `_get_dag_run()` function from [airflow/cli/commands/task_command.py](https://github.com/apache/airflow/blob/bb82cc0fbb7a6630eac1155d0c3b445dff13ceb6/airflow/cli/commands/task_command.py#L61-L72): ``` execution_date = None with suppress(ParserError, TypeError): execution_date = timezone.parse(exec_date_or_run_id) if create_if_necessary and not execution_date: return DagRun(dag_id=dag.dag_id, run_id=exec_date_or_run_id) try: return ( session.query(DagRun) .filter( DagRun.dag_id == dag.dag_id, DagRun.execution_date == execution_date, ) .one() ) ``` Here, `exec_date_or_run_id == 'manual__2021-12-17T15:01:08.943254+00:00'` and `timezone.parse(exec_date_or_run_id)` fails, meaning `execution_date` stays as `None` and the session query returns no results. ### What you expected to happen Expect DAGs to run without the above error. ### How to reproduce Upgraded from 2.1.4 to 2.2.2 and manually ran a few DAGs. The above log is from the [example_python_operator](https://github.com/apache/airflow/blob/main/airflow/example_dags/example_python_operator.py) DAG provided on the Airflow repo. ### Operating System Amazon Linux 2 ### Versions of Apache Airflow Providers _No response_ ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else Tried `airflow db upgrade` and `airflow db reset` without any luck. The same issue appears on 2.2.3rc2. Using MySQL 8.0.23. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20384

https://github.com/apache/airflow/pull/20737

384fa4a87dfaa79a89ad8e18ac1980e07badec4b

7947b72eee61a4596c5d8667f8442d32dcbf3f6d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/callModal.js", "airflow/www/static/js/dag.js", "airflow/www/static/js/gantt.js", "airflow/www/static/js/graph.js"]

Paused event is logging twice in some pages.

### Apache Airflow version 2.2.2 (latest released) ### What happened If I pause/unpause dag in `Tree`, `Graph`, or `Gantt` page, `/paused` url is calling twice.  So paused events are logging twice.  ### What you expected to happen It should be logging only once. ### How to reproduce Go to `Tree`, `Graph`, or `Gantt` page. And pause/unpause it. ### Operating System centos ### Versions of Apache Airflow Providers _No response_ ### Deployment Other 3rd-party Helm chart ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20373

https://github.com/apache/airflow/pull/28410

9c3734bb127ff0d71a0321d0578e556552cfc934

2f0f02536f7773dd782bd980ae932091b7badc61

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/transfers/gcs_to_bigquery.py"]

GCSToBigQueryOperator not rendering list pulled from XCOM

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers _No response_ ### Apache Airflow version 2.1.4 ### Operating System Container-Optimized OS with Containerd ### Deployment Composer ### Deployment details _No response_ ### What happened I am trying to load some files from GCS to BigQuery. To get the list of files which matches the prefix, I am using the GoogleCloudStorageListOperator which pushes the output to XCOM. XCOM Push from GoogleCloudStorageListOperator: ['file1.csv', 'file2.csv', 'file3.csv', 'file4.csv', 'file5.csv'] When I am pulling the list from XCOM to use for BigQuery Load operation, it is getting rendered like: **[**['file1.csv', 'file2.csv', 'file3.csv', 'file4.csv', 'file5.csv']**]** Due to this I am getting the below error in GCSToBigQueryOperator : Source URI must not contain the ',' character: gs://inbound-bucket/[['file1.csv', 'file2.csv', 'file3.csv', 'file4.csv', 'file5.csv']] The code I am using can be found below. _dag = DAG(AIRFLOW_DAG_ID, default_args = default_values, description = "google_operator_test", catchup = False, max_active_runs = 1 ,render_template_as_native_obj=True, schedule_interval = None) get_gcs_file_list = GoogleCloudStorageListOperator( task_id= "get_gcs_file_list_1", google_cloud_storage_conn_id = "temp_google_access", bucket= "inbound-bucket", prefix= source_object_location, delimiter= '.csv', dag = dag) #Load the data from GCS to BQ load_to_bq_stage = GCSToBigQueryOperator( task_id= "bqload", bigquery_conn_id = "temp_google_access", google_cloud_storage_conn_id = "temp_google_access", bucket= "inbound-bucket", source_objects= '{{task_instance.xcom_pull(task_ids="get_gcs_file_list_1")}}', destination_project_dataset_table= dataset + "." + target_table, write_disposition='WRITE_TRUNCATE', field_delimiter = '|', dag=dag)_ ### What you expected to happen The list should be rendered as is by the GCSToBigQueryOperator. ### How to reproduce 1. Push a list of strings in to XCOM 2. Perform XCOM pull in source_objects parameter of GCSToBigQueryOperator like below, source_objects ='{{task_instance.xcom_pull(task_ids="task_name")}}' 3. Notice that the rendered source_objects have an extra parenthesis added. ### Anything else There is a validation performed in the operator’s constructor to check whether or not the source_objecst datatype is a list. If is it not a list, then it’s presumed to be a string and wrap it as a list.Because source_objects is a template field, the field value isn’t evaluated until the task runs (aka meaning the value isn’t rendered until the execute() method of the operator). https://github.com/apache/airflow/blob/6eac2e0807a8be5f39178f079db28ebcd2f83621/airflow/providers/google/cloud/transfers/gcs_to_bigquery.py#L219 ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20333

https://github.com/apache/airflow/pull/20347

6e51608f28f4c769c019624ea0caaa0c6e671f80

17404f1f10efd41f98eb8a0317b578ff40f9c77d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/decorators/__init__.py", "airflow/decorators/__init__.pyi", "airflow/decorators/sensor.py", "airflow/example_dags/example_sensor_decorator.py", "airflow/sensors/python.py", "docs/apache-airflow/tutorial/taskflow.rst", "tests/decorators/test_sensor.py"]

Add a `@task.sensor` TaskFlow decorator

### Description Implement a taskflow decorator that uses the decorated function as the poke method. ### Use case/motivation Here is a sketch of the solution that might work: [sensor_decorator.txt](https://github.com/apache/airflow/files/7721589/sensor_decorator.txt) ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20323

https://github.com/apache/airflow/pull/22562

a50195d617ca7c85d56b1c138f46451bc7599618

cfd63df786e0c40723968cb8078f808ca9d39688

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/api_connexion/openapi/v1.yaml", "airflow/www/static/js/types/api-generated.ts", "tests/api_connexion/endpoints/test_user_endpoint.py"]

Airflow API throwing 500 when user does not have last name

### Apache Airflow version 2.2.2 (latest released) ### What happened When listing users via GET api/v1/users, a 500 error is thrown if a user in the database does not have a last name. ``` { "detail": "'' is too short\n\nFailed validating 'minLength' in schema['allOf'][0]['properties']['users']['items']['properties']['last_name']:\n {'description': 'The user lastname', 'minLength': 1, 'type': 'string'}\n\nOn instance['users'][25]['last_name']:\n ''", "status": 500, "title": "Response body does not conform to specification", "type": "https://airflow.apache.org/docs/apache-airflow/2.2.2/stable-rest-api-ref.html#section/Errors/Unknown" } ``` ### What you expected to happen Result set should still be returned with a null or empty value for the last name instead of a 500 error. ### How to reproduce Create a user in the database without a last name and then hit GET api/v1/users ### Operating System linux ### Versions of Apache Airflow Providers _No response_ ### Deployment Other Docker-based deployment ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20320

https://github.com/apache/airflow/pull/25476

98f16aa7f3b577022791494e13b6aa7057afde9d

3421ecc21bafaf355be5b79ec4ed19768e53275a

python

closed

apache/airflow

https://github.com/apache/airflow

["Dockerfile", "Dockerfile.ci", "breeze", "dev/README_RELEASE_PROVIDER_PACKAGES.md", "docs/docker-stack/build-arg-ref.rst", "scripts/ci/libraries/_build_images.sh", "scripts/ci/libraries/_initialization.sh", "scripts/docker/compile_www_assets.sh", "scripts/docker/install_airflow.sh", "scripts/docker/prepare_node_modules.sh"]

Make sure that stderr is "clean" while building the images.

Our image generates a lot of "Noise" during building, which makes "real" errors difficult to distinguish from "false negatives". We should make sure that there are no warnings while the docker image is build. If there are any warnings that we cannot remove there, we should add a reassuring note that we know what we are doing.

https://github.com/apache/airflow/issues/20259

https://github.com/apache/airflow/pull/20238

5980d2b05eee484256c634d5efae9410265c65e9

4620770af4550251b5139bb99185656227335f67

python

closed

apache/airflow

https://github.com/apache/airflow

[".github/workflows/ci.yml", "dev/breeze/setup.cfg", "scripts/ci/images/ci_test_examples_of_prod_image_building.sh"]

CI: Rewrite testing examples of images in python

We are runing automated tests for example images building as Bash scripts - those should be converted to Python. https://github.com/apache/airflow/blob/98514cc1599751d7611b3180c60887da0a25ff5e/.github/workflows/ci.yml#L322

https://github.com/apache/airflow/issues/20254

https://github.com/apache/airflow/pull/21097

bc1f062bdebd5a92b650e2316d4d98d2097388ca

0d623296f1e5b6354e37dc0a45b2e4ed1a13901e

python

closed

apache/airflow

https://github.com/apache/airflow

["dev/breeze/src/airflow_breeze/breeze.py", "dev/breeze/src/airflow_breeze/cache.py", "dev/breeze/src/airflow_breeze/global_constants.py", "dev/breeze/src/airflow_breeze/visuals/__init__.py"]

Breeze: Asciiart disabling

This is a small thing, but might be useful. Currently Breeze prints the Asciiart when started. You should be able to persistently disable the asciiart. This is done with storing the rigth "flag" file in the ".build" directory. If the file is there, the ASCIIART should not be printed. If not, it shoudl be printed.

https://github.com/apache/airflow/issues/20252

https://github.com/apache/airflow/pull/20645

c9023fad4287213e4d3d77f4c66799c762bff7ba

8cc93c4bc6ae5a99688ca2effa661d6a3e24f56f

python

closed

apache/airflow

https://github.com/apache/airflow

["dev/breeze/doc/BREEZE2.md", "scripts/ci/libraries/_docker_engine_resources.sh", "scripts/in_container/run_resource_check.py", "scripts/in_container/run_resource_check.sh"]

Breeze: Verify if there are enough resources avaiilable in Breeze

At entry of the Breeze command we verify if there iss enouggh CPU/memory/disk space and print (coloured) information if the resources are not enough. We should replicate that in Python

https://github.com/apache/airflow/issues/20251

https://github.com/apache/airflow/pull/20763

b8526abc2c220b1e07eed83694dfee972c2e2609

75755d7f65fb06c6e2e74f805b877774bfa7fcda

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/jobs/base_job.py", "airflow/migrations/versions/587bdf053233_adding_index_for_dag_id_in_job.py", "docs/apache-airflow/migrations-ref.rst"]

DAG deletion is slow due to lack of database indexes on dag_id

### Apache Airflow version 2.2.1 ### What happened We have an airflow instance for approximately 6k DAGs. - If we delete a DAG from UI, the UI times out - If we delete a DAG from CLI, it completes but sometimes takes up to a half-hour to finish. Most of the execution time appears to be consumed in database queries. I know I can just throw more CPU and memory to the db instance and hope it works but I think we can do better during delete operation. Correct me if I am wrong but I think this is the code that gets executed when deleting a DAG from UI or CLI via `delete_dag.py` ```python for model in models.base.Base._decl_class_registry.values(): if hasattr(model, "dag_id"): if keep_records_in_log and model.__name__ == 'Log': continue cond = or_(model.dag_id == dag_id, model.dag_id.like(dag_id + ".%")) count += session.query(model).filter(cond).delete(synchronize_session='fetch') if dag.is_subdag: parent_dag_id, task_id = dag_id.rsplit(".", 1) for model in TaskFail, models.TaskInstance: count += ( session.query(model).filter(model.dag_id == parent_dag_id, model.task_id == task_id).delete() ) ``` I see we are iterating over all the models and doing a `dag_id` match. Some of the tables don't have an index over `dag_id` column like `job` which is making this operation really slow. This could be one easy fix for this issue. For example, the following query took 20 mins to finish in 16cpu 32gb Postgres instance: ```sql SELECT job.id AS job_id FROM job WHERE job.dag_id = $1 OR job.dag_id LIKE $2 ``` and explain is as follows ```sql EXPLAIN SELECT job.id AS job_id FROM job WHERE job.dag_id = ''; QUERY PLAN --------------------------------------------------------------------------- Gather (cost=1000.00..1799110.10 rows=6351 width=8) Workers Planned: 2 -> Parallel Seq Scan on job (cost=0.00..1797475.00 rows=2646 width=8) Filter: ((dag_id)::text = ''::text) (4 rows) ``` This is just one of the many queries that are being executed during the delete operation. ### What you expected to happen Deletion of DAG should not take this much time. ### How to reproduce _No response_ ### Operating System nix ### Versions of Apache Airflow Providers _No response_ ### Deployment Other Docker-based deployment ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20249

https://github.com/apache/airflow/pull/20282

6d25d63679085279ca1672c2eee2c45d6704efaa

ac9f29da200c208bb52d412186c5a1b936eb0b5a

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/example_dags/example_kubernetes_executor.py", "airflow/example_dags/example_python_operator.py", "airflow/example_dags/tutorial_taskflow_api_etl_virtualenv.py"]

Various example dag errors on db init on fresh install

### Apache Airflow version 2.2.3rc1 (release candidate) ### What happened After fresh install of the latest release and running db init it getting below error messages among usual db init messages. Note that I deliberately didn't chose to use VENV but I think that should be handled gracefully rather than below errors. Also relates to https://github.com/apache/airflow/pull/19355 ```ERROR [airflow.models.dagbag.DagBag] Failed to import: /usr/local/lib/python3.8/site-packages/airflow/example_dags/tutorial_taskflow_api_etl_virtualenv.py Traceback (most recent call last): File "/usr/local/lib/python3.8/site-packages/airflow/models/dagbag.py", line 331, in _load_modules_from_file loader.exec_module(new_module) File "<frozen importlib._bootstrap_external>", line 843, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/usr/local/lib/python3.8/site-packages/airflow/example_dags/tutorial_taskflow_api_etl_virtualenv.py", line 81, in <module> tutorial_etl_dag = tutorial_taskflow_api_etl_virtualenv() File "/usr/local/lib/python3.8/site-packages/airflow/models/dag.py", line 2984, in factory f(**f_kwargs) File "/usr/local/lib/python3.8/site-packages/airflow/example_dags/tutorial_taskflow_api_etl_virtualenv.py", line 76, in tutorial_taskflow_api_etl_virtualenv order_data = extract() File "/usr/local/lib/python3.8/site-packages/airflow/decorators/base.py", line 219, in factory op = decorated_operator_class( File "/usr/local/lib/python3.8/site-packages/airflow/models/baseoperator.py", line 188, in apply_defaults result = func(self, *args, **kwargs) File "/usr/local/lib/python3.8/site-packages/airflow/decorators/python_virtualenv.py", line 61, in __init__ super().__init__(kwargs_to_upstream=kwargs_to_upstream, **kwargs) File "/usr/local/lib/python3.8/site-packages/airflow/models/baseoperator.py", line 188, in apply_defaults result = func(self, *args, **kwargs) File "/usr/local/lib/python3.8/site-packages/airflow/decorators/base.py", line 131, in __init__ super().__init__(**kwargs_to_upstream, **kwargs) File "/usr/local/lib/python3.8/site-packages/airflow/models/baseoperator.py", line 188, in apply_defaults result = func(self, *args, **kwargs) File "/usr/local/lib/python3.8/site-packages/airflow/operators/python.py", line 371, in __init__ raise AirflowException('PythonVirtualenvOperator requires virtualenv, please install it.') airflow.exceptions.AirflowException: PythonVirtualenvOperator requires virtualenv, please install it. ERROR [airflow.models.dagbag.DagBag] Failed to import: /usr/local/lib/python3.8/site-packages/airflow/example_dags/example_python_operator.py Traceback (most recent call last): File "/usr/local/lib/python3.8/site-packages/airflow/models/dagbag.py", line 331, in _load_modules_from_file loader.exec_module(new_module) File "<frozen importlib._bootstrap_external>", line 843, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/usr/local/lib/python3.8/site-packages/airflow/example_dags/example_python_operator.py", line 86, in <module> virtualenv_task = callable_virtualenv() File "/usr/local/lib/python3.8/site-packages/airflow/decorators/base.py", line 219, in factory op = decorated_operator_class( File "/usr/local/lib/python3.8/site-packages/airflow/models/baseoperator.py", line 188, in apply_defaults result = func(self, *args, **kwargs) File "/usr/local/lib/python3.8/site-packages/airflow/decorators/python_virtualenv.py", line 61, in __init__ super().__init__(kwargs_to_upstream=kwargs_to_upstream, **kwargs) File "/usr/local/lib/python3.8/site-packages/airflow/models/baseoperator.py", line 188, in apply_defaults result = func(self, *args, **kwargs) File "/usr/local/lib/python3.8/site-packages/airflow/decorators/base.py", line 131, in __init__ super().__init__(**kwargs_to_upstream, **kwargs) File "/usr/local/lib/python3.8/site-packages/airflow/models/baseoperator.py", line 188, in apply_defaults result = func(self, *args, **kwargs) File "/usr/local/lib/python3.8/site-packages/airflow/operators/python.py", line 371, in __init__ raise AirflowException('PythonVirtualenvOperator requires virtualenv, please install it.') airflow.exceptions.AirflowException: PythonVirtualenvOperator requires virtualenv, please install it. WARNI [unusual_prefix_97712dd65736a8bf7fa3a692877470abf8eebade_example_kubernetes_executor] Could not import DAGs in example_kubernetes_executor.py Traceback (most recent call last): File "/usr/local/lib/python3.8/site-packages/airflow/example_dags/example_kubernetes_executor.py", line 36, in <module> from kubernetes.client import models as k8s ModuleNotFoundError: No module named 'kubernetes' WARNI [unusual_prefix_97712dd65736a8bf7fa3a692877470abf8eebade_example_kubernetes_executor] Install Kubernetes dependencies with: pip install apache-airflow[cncf.kubernetes] ERROR [airflow.models.dagbag.DagBag] Failed to import: /usr/local/lib/python3.8/site-packages/airflow/example_dags/example_kubernetes_executor.py Traceback (most recent call last): File "/usr/local/lib/python3.8/site-packages/airflow/models/dagbag.py", line 331, in _load_modules_from_file loader.exec_module(new_module) File "<frozen importlib._bootstrap_external>", line 843, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/usr/local/lib/python3.8/site-packages/airflow/example_dags/example_kubernetes_executor.py", line 51, in <module> "pod_override": k8s.V1Pod(metadata=k8s.V1ObjectMeta(annotations={"test": "annotation"})) NameError: name 'k8s' is not defined ``` ### What you expected to happen No errors, but warnings ### How to reproduce docker run --name test-airflow-2.2.3rc1 -it python:3.8.12-buster bash airflow db init pip install apache-airflow==2.2.3rc1 ### Operating System debian ### Versions of Apache Airflow Providers _No response_ ### Deployment Other Docker-based deployment ### Deployment details docker run --name test-airflow-2.2.3rc1 -it python:3.8.12-buster bash ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20225

https://github.com/apache/airflow/pull/20295

60b72dd8f5161f0e00f29a9de54522682f7cd5f6

5a6c022f946d1be2bd68a42a7a920fdf932932e5

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/amazon/aws/example_dags/example_emr_serverless.py", "airflow/providers/amazon/aws/hooks/emr.py", "airflow/providers/amazon/aws/operators/emr.py", "airflow/providers/amazon/aws/sensors/emr.py", "airflow/providers/amazon/provider.yaml", "docs/apache-airflow-providers-amazon/operators/emr_serverless.rst", "tests/providers/amazon/aws/hooks/test_emr_serverless.py", "tests/providers/amazon/aws/operators/test_emr_serverless.py"]

EMR serverless, new operator

### Description A new EMR serverless has been announced and it is already available, see: - https://aws.amazon.com/blogs/big-data/announcing-amazon-emr-serverless-preview-run-big-data-applications-without-managing-servers/ - https://aws.amazon.com/emr/serverless/ Having an operator for creating applications and submitting jobs to EMR serverless would be awesome. ### Use case/motivation New operator for working with EMR serverless. ### Related issues _No response_ ### Are you willing to submit a PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20215

https://github.com/apache/airflow/pull/25324

5480b4ca499cfe37677ac1ae1298a2737a78115d

8df84e99b7319740990124736d0fc545165e7114

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/templates/airflow/dag.html", "tests/www/views/test_views_tasks.py"]

Triggering a DAG in Graph view switches to Tree view

### Apache Airflow version 2.2.2 (latest released) ### What happened After I trigger a DAG from the Graph view, the view switches to the Tree view (happens on both 2.2.2 and main). ### What you expected to happen I expect to stay in the Graph view, ideally switch to the newly created DAG run ### How to reproduce Trigger a DAG from the Graph view ### Operating System N/A ### Versions of Apache Airflow Providers N/A ### Deployment Astronomer ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20197

https://github.com/apache/airflow/pull/20955

10f5db863e387c0fd7369cf521d624b6df77a65d

928dafe6c495bbf3e03d14473753fce915134a46

python

closed

apache/airflow

https://github.com/apache/airflow

["setup.py"]

ssl_cert_reqs has been deprecated in pymongo 3.9

### Apache Airflow Provider(s) mongo ### Versions of Apache Airflow Providers 2.2.0 ### Apache Airflow version 2.2.2 (latest released) ### Operating System Debian GNU/Linux 10 (buster) ### Deployment Official Apache Airflow Helm Chart ### Deployment details _No response_ ### What happened `ssl_cert_reqs` has been deprecated in pymongo 3.9 https://github.com/apache/airflow/blob/providers-mongo/2.2.0/airflow/providers/mongo/hooks/mongo.py#L94 ``` {taskinstance.py:1703} ERROR - Task failed with exception Traceback (most recent call last): File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1332, in _run_raw_task self._execute_task_with_callbacks(context) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1458, in _execute_task_with_callbacks result = self._execute_task(context, self.task) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1509, in _execute_task result = execute_callable(context=context) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/amazon/aws/transfers/mongo_to_s3.py", line 123, in execute mongo_db=self.mongo_db, File "/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/mongo/hooks/mongo.py", line 145, in find collection = self.get_collection(mongo_collection, mongo_db=mongo_db) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/mongo/hooks/mongo.py", line 116, in get_collection mongo_conn: MongoClient = self.get_conn() File "/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/mongo/hooks/mongo.py", line 96, in get_conn self.client = MongoClient(self.uri, **options) File "/home/airflow/.local/lib/python3.7/site-packages/pymongo/mongo_client.py", line 707, in __init__ keyword_opts.cased_key(k), v) for k, v in keyword_opts.items())) File "/home/airflow/.local/lib/python3.7/site-packages/pymongo/mongo_client.py", line 707, in <genexpr> keyword_opts.cased_key(k), v) for k, v in keyword_opts.items())) File "/home/airflow/.local/lib/python3.7/site-packages/pymongo/common.py", line 740, in validate value = validator(option, value) File "/home/airflow/.local/lib/python3.7/site-packages/pymongo/common.py", line 144, in raise_config_error raise ConfigurationError("Unknown option %s" % (key,)) pymongo.errors.ConfigurationError: Unknown option ssl_cert_reqs ``` Ref: https://pymongo.readthedocs.io/en/stable/changelog.html#changes-in-version-3-9-0 ### What you expected to happen _No response_ ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20155

https://github.com/apache/airflow/pull/20511

86a249007604307bdf0f69012dbd1b783c8750e5

f85880e989d7751cfa3ae2d4665d7cc0cb3cc945

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/extensions/init_views.py", "tests/api_connexion/test_cors.py"]

CORS access_control_allow_origin header never returned

### Apache Airflow version 2.2.2 (latest released) ### What happened To fix CORS problem added the [access_control_allow_headers](https://airflow.apache.org/docs/apache-airflow/stable/configurations-ref.html#access-control-allow-headers), [access_control_allow_methods](https://airflow.apache.org/docs/apache-airflow/stable/configurations-ref.html#access-control-allow-methods), [access_control_allow_origins](https://airflow.apache.org/docs/apache-airflow/stable/configurations-ref.html#access-control-allow-origins) variables to the 2.2.2 docker-compose file provided in documentation. Both header, and methods returns with the correct value, but origins never does. ### What you expected to happen The CORS response returning with provided origin header value. ### How to reproduce Download the latest docker-compose from documentation add the following lines: `AIRFLOW__API__ACCESS_CONTROL_ALLOW_HEADERS: 'content-type, origin, authorization, accept'` `AIRFLOW__API__ACCESS_CONTROL_ALLOW_METHODS: 'GET, POST, OPTIONS, DELETE'` `AIRFLOW__API__ACCESS_CONTROL_ALLOW_ORIGINS: '*'` run and call with a CORS preflight ### Operating System Windows 11 ### Versions of Apache Airflow Providers _No response_ ### Deployment Docker-Compose ### Deployment details _No response_ ### Anything else It's repeatable regardless of ORIGINS value. There was a name change on this variable that's possibly not handled. On 2.1.4 the same works without problems. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20110

https://github.com/apache/airflow/pull/25553

1d8507af07353e5cf29a860314b5ba5caad5cdf3

e81b27e713e9ef6f7104c7038f0c37cc55d96593

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/jobs/scheduler_job.py", "tests/jobs/test_scheduler_job.py"]

Scheduler crashlooping when dag with task_concurrency is deleted

### Apache Airflow version 2.2.2 (latest released) ### What happened After deleting the dag, the scheduler starts crashlooping and cannot recover. This means that an issue with the dag causes the whole environment to be down. The stacktrace is as follows: airflow-scheduler [2021-12-07 09:30:07,483] {kubernetes_executor.py:791} INFO - Shutting down Kubernetes executor airflow-scheduler [2021-12-07 09:30:08,509] {process_utils.py:100} INFO - Sending Signals.SIGTERM to GPID 1472 airflow-scheduler [2021-12-07 09:30:08,681] {process_utils.py:66} INFO - Process psutil.Process(pid=1472, status='terminated', exitcode=0, started='09:28:37') (1472) terminated with exit airflow-scheduler [2021-12-07 09:30:08,681] {scheduler_job.py:655} INFO - Exited execute loop airflow-scheduler Traceback (most recent call last): airflow-scheduler File "/home/airflow/.local/bin/airflow", line 8, in <module> airflow-scheduler sys.exit(main()) airflow-scheduler File "/home/airflow/.local/lib/python3.9/site-packages/airflow/__main__.py", line 48, in main airflow-scheduler args.func(args) airflow-scheduler File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/cli_parser.py", line 48, in command airflow-scheduler return func(*args, **kwargs) airflow-scheduler File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/cli.py", line 92, in wrapper airflow-scheduler return f(*args, **kwargs) airflow-scheduler File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/commands/scheduler_command.py", line 75, in scheduler airflow-scheduler _run_scheduler_job(args=args) airflow-scheduler File "/home/airflow/.local/lib/python3.9/site-packages/airflow/cli/commands/scheduler_command.py", line 46, in _run_scheduler_job airflow-scheduler job.run() airflow-scheduler File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/base_job.py", line 245, in run airflow-scheduler self._execute() airflow-scheduler File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 628, in _execute airflow-scheduler self._run_scheduler_loop() airflow-scheduler File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 709, in _run_scheduler_loop airflow-scheduler num_queued_tis = self._do_scheduling(session) airflow-scheduler File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 820, in _do_scheduling airflow-scheduler num_queued_tis = self._critical_section_execute_task_instances(session=session) airflow-scheduler File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 483, in _critical_section_execute_task_instances airflow-scheduler queued_tis = self._executable_task_instances_to_queued(max_tis, session=session) airflow-scheduler File "/home/airflow/.local/lib/python3.9/site-packages/airflow/utils/session.py", line 67, in wrapper airflow-scheduler return func(*args, **kwargs) airflow-scheduler File "/home/airflow/.local/lib/python3.9/site-packages/airflow/jobs/scheduler_job.py", line 366, in _executable_task_instances_to_queued airflow-scheduler if serialized_dag.has_task(task_instance.task_id): airflow-scheduler AttributeError: 'NoneType' object has no attribute 'has_task' ### What you expected to happen I expect that the scheduler does not crash because the dag gets deleted. The biggest issue however is that the whole environment goes down, it would be acceptable that the scheduler has issues with that dag (it is deleted after all) but it should not affect all other dags on the environment. ### How to reproduce 1. I created the following dag: ` from airflow import DAG from datafy.operators import DatafyContainerOperatorV2 from datetime import datetime, timedelta default_args = { "owner": "Datafy", "depends_on_past": False, "start_date": datetime(year=2021, month=12, day=1), "task_concurrency": 4, "retries": 2, "retry_delay": timedelta(minutes=5), } dag = DAG( "testnielsdev", default_args=default_args, max_active_runs=default_args["task_concurrency"] + 1, schedule_interval="0 1 * * *", ) DatafyContainerOperatorV2( dag=dag, task_id="sample", cmds=["python"], arguments=["-m", "testnielsdev.sample", "--date", "{{ ds }}", "--env", "{{ macros.datafy.env() }}"], instance_type="mx_small", instance_life_cycle="spot", ) ` When looking at the airflow code, the most important setting apart from the defaults is to specify task_concurrency. 2. I enable the dag 3. I delete it. When the file gets removed, the scheduler starts crashlooping. ### Operating System We use the default airflow docker image ### Versions of Apache Airflow Providers Not relevant ### Deployment Other Docker-based deployment ### Deployment details Not relevant ### Anything else It occurred at one of our customers and I was quickly able to preproduce the issue. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20099

https://github.com/apache/airflow/pull/20349

dba00ce6a32b7f50153887c6974f62985ca8023f

98715760f72e5205c291293088b5e79636884491

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/configuration.py", "docs/apache-airflow/howto/set-config.rst", "tests/core/test_configuration.py"]

PR #18772 breaks `sql_alchemy_conn_cmd` config

### Apache Airflow version 2.2.1 ### What happened #18772 added two options to `tmp_configuration_copy()` and defaults tasks run as the same user to generate the temp configuration file for the task runner with the options `include_env=False` and `include_cmds=False`. This is a change from the previous defaults which materialized the values of `*_cmd` configs into the JSON dump. This presents a problem because when using `sql_alchemy_conn_cmd` to set the database connection and running as the same user the temporary config JSON dump now includes both `sql_alchemy_conn`, set to the Airflow distribution default as well as the user set `sql_alchemy_conn_cmd`. And because bare settings take precedence over `_cmd` versions while the Airflow worker can connect to the configured DB the task runner itself will instead use a non-existent SQLite DB causing all tasks to fail. TLDR; The temp JSON config dump used to look something like: ``` { "core": { "sql_alchemy_conn": "mysql://...", ... } } ``` Now it looks something like: ``` { "core": { "sql_alchemy_conn": "sqlite:////var/opt/airflow/airflow.db", "sql_alchemy_conn_cmd": "/etc/airflow/get-config 'core/sql_alchemy_conn'" ... } } ``` But because `sql_alchemy_conn` is set `sql_alchemy_conn_cmd` never gets called and tasks are unable to access the Airflow DB. ### What you expected to happen I'm not quite sure what is the preferred method to fix this issue. I guess there are a couple options: - Remove the bare `sensitive_config_values` if either `_cmd` or `_secret` versions exist - Ignore the Airflow default value in addition to empty values for bare `sensitive_config_values` during parsing - Go back to materializing the sensitive configs ### How to reproduce With either Airflow 2.2.1 or 2.2.2 configure the DB with `sql_alchemy_conn_cmd` and remove the `sql_alchemy_conn` config from your config file. Then run the Airflow worker and task runner as the same user. Try running any task and see that the task tries to access the default SQLite store instead of the configured one. ### Operating System Debian Bullseye ### Versions of Apache Airflow Providers _No response_ ### Deployment Virtualenv installation ### Deployment details _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20092

https://github.com/apache/airflow/pull/21539

e93cd4b2cfa98be70f6521832cfbd4d6b5551e30

e07bc63ec0e5b679c87de8e8d4cdff1cf4671146

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/dag.py", "airflow/www/views.py"]

Forward slash in `dag_run_id` gives rise to trouble accessing things through the REST API

### Apache Airflow version 2.1.4 ### Operating System linux ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==2.2.0 apache-airflow-providers-celery==2.0.0 apache-airflow-providers-cncf-kubernetes==2.0.2 apache-airflow-providers-docker==2.1.1 apache-airflow-providers-elasticsearch==2.0.3 apache-airflow-providers-ftp==2.0.1 apache-airflow-providers-google==5.1.0 apache-airflow-providers-grpc==2.0.1 apache-airflow-providers-hashicorp==2.1.0 apache-airflow-providers-http==2.0.1 apache-airflow-providers-imap==2.0.1 apache-airflow-providers-microsoft-azure==3.1.1 apache-airflow-providers-mysql==2.1.1 apache-airflow-providers-postgres==2.2.0 apache-airflow-providers-redis==2.0.1 apache-airflow-providers-sendgrid==2.0.1 apache-airflow-providers-sftp==2.1.1 apache-airflow-providers-slack==4.0.1 apache-airflow-providers-sqlite==2.0.1 apache-airflow-providers-ssh==2.1.1 ### Deployment Docker-Compose ### Deployment details We tend to trigger dag runs by some external event, e.g., a media-file upload, see #19745. It is useful to use the media-file path as a dag run id. The media-id can come with some partial path, e.g., `path/to/mediafile`. All this seems to work fine in airflow, but we can't figure out a way to use the such a dag run id in the REST API, as the forward slashes `/` interfere with the API routing. ### What happened When using the API route `api/v1/dags/{dag_id}/dagRuns/{dag_run_id}` in, e.g., a HTTP GET, we expect a dag run to be found when `dag_run_id` has the value `path/to/mediafile`, but instead a `.status: 404` is returned. When we change the `dag_run_id` to the format `path|to|mediafile`, the dag run is returned. ### What you expected to happen We would expect a dag run to be returned, even if it contains the character `/` ### How to reproduce Trigger a dag using a dag_run_id that contains a `/`, then try to retrieve it though the REST API. ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20063

https://github.com/apache/airflow/pull/23106

ebc1f14db3a1b14f2535462e97a6407f48b19f7c

451c7cbc42a83a180c4362693508ed33dd1d1dab

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/snowflake/hooks/snowflake.py", "tests/providers/snowflake/hooks/test_snowflake.py"]

Snowflake Provider - Hook's support for not providing a region is broken when using SQLAlchemy

### Apache Airflow Provider(s) snowflake ### Versions of Apache Airflow Providers Versions 2.2.x (since https://github.com/apache/airflow/commit/0a37be3e3cf9289f63f1506bc31db409c2b46738). ### Apache Airflow version 2.2.1 ### Operating System Debian GNU/Linux 10 (buster) ### Deployment Other 3rd-party Helm chart ### Deployment details Bitnami Airflow Helm chart @ version 8.0.2 ### What happened When connecting to Snowflake via SQLAlchemy using the Snowflake Hook, I get an error that the URL is not valid because my Snowflake instance is in US West 2 (Oregon) which means I don't provide a region explicitly. Snowflake's documentation says: > If the account is located in the AWS US West (Oregon) region, no additional segments are required and the URL would be xy12345.snowflakecomputing.com The error is that `xy12345..snowflakecomputing.com` is not a valid URL (note the double-dot caused by the lack of a region). ### What you expected to happen I expect the connection to be successful. ### How to reproduce You can use the default snowflake connection if you have one defined and see this problem with the following one-liner: ```shell python -c 'from airflow.providers.snowflake.hooks.snowflake import SnowflakeHook; SnowflakeHook().get_sqlalchemy_engine().connect()' ``` ### Anything else Fortunately I imagine the fix for this is just to leave the region URL component out when `region` is `None` here: https://github.com/apache/airflow/commit/0a37be3e3cf9289f63f1506bc31db409c2b46738#diff-2b674ac999a5b938fe5045f6475b0c5cc76e4cab89174ac448a9e1d41a5c04d5R215. Using version `2.1.1` of the Snowflake provider with version `2.2.1` of Airflow is currently a viable workaround so for now I am just avoiding the update to the provider. ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20032

https://github.com/apache/airflow/pull/20509

b7086f9815d3856cb4f3ee5bbc78657f19df9d2d

a632b74846bae28408fb4c1b38671fae23ca005c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/transfers/postgres_to_gcs.py"]

PostgresToGCSOperator fail on empty table and use_server_side_cursor=True

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers apache-airflow-providers-google==6.1.0 ### Apache Airflow version 2.2.2 (latest released) ### Operating System Debian GNU/Linux 10 (buster) ### Deployment Other Docker-based deployment ### Deployment details _No response_ ### What happened When I'm execute `PostgresToGCSOperator` on empty table and set `use_server_side_cursor=True` the operator fails with error: ``` Traceback (most recent call last): File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1332, in _run_raw_task self._execute_task_with_callbacks(context) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1458, in _execute_task_with_callbacks result = self._execute_task(context, self.task) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/models/taskinstance.py", line 1514, in _execute_task result = execute_callable(context=context) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/google/cloud/transfers/sql_to_gcs.py", line 154, in execute files_to_upload = self._write_local_data_files(cursor) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/google/cloud/transfers/sql_to_gcs.py", line 213, in _write_local_data_files row = self.convert_types(schema, col_type_dict, row) File "/home/airflow/.local/lib/python3.9/site-packages/airflow/providers/google/cloud/transfers/sql_to_gcs.py", line 174, in convert_types return [self.convert_type(value, col_type_dict.get(name)) for name, value in zip(schema, row)] TypeError: 'NoneType' object is not iterable ``` Operator command when I'm using: ```python task_send = PostgresToGCSOperator( task_id=f'send_{table}', postgres_conn_id='postgres_raw', gcp_conn_id=gcp_conn_id, sql=f'SELECT * FROM public.{table}', use_server_side_cursor=True, bucket=bucket, filename=f'{table}.csv', export_format='csv', ) ``` ### What you expected to happen I'm expected, that operator on empty table not creating file and no upload it on Google Cloud. ### How to reproduce - Create empty postgresql table. - Create dag with task with PostgresToGCSOperator. that upload this table in Google Cloud. ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/20007

https://github.com/apache/airflow/pull/21307

00f0025abf6500af67f4c5b7543d45658d31b3b2

2eb10565b2075d89eb283bd53462c00f5d54ab55

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/static/js/dag_dependencies.js"]

Arrows in DAG dependencies view are not consistent

### Apache Airflow version main (development) ### Operating System N/A ### Versions of Apache Airflow Providers N/A ### Deployment Other ### Deployment details _No response_ ### What happened The arrows in the DAG dependencies view are not consistent with the graph view: Graph View:  DAG dependencies view:  ### What you expected to happen All arrows should be filled black ### How to reproduce _No response_ ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/19989

https://github.com/apache/airflow/pull/20303

2d9338f2b7fa6b7aadb6a81cd5fc3b3ad8302a4a

28045696dd3ea7207b1162c2343ba142e1f75e5d

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/db.py"]

Migrations fail due to GTID Consistency Violation when using GTID Replication

### Apache Airflow version 2.2.2 (latest released) ### Operating System Debian GNU/Linux 10 (buster) ### Versions of Apache Airflow Providers _No response_ ### Deployment Other 3rd-party Helm chart ### Deployment details Using MySQL 8.0 via Google CloudSQL ### What happened When upgrading from 2.1.2 to 2.2.2 the `airflow db upgrade` command failed with the following command: ``` sqlalchemy.exc.OperationalError: (MySQLdb._exceptions.OperationalError) (1786, 'Statement violates GTID consistency: CREATE TABLE ... SELECT.') [SQL: create table _airflow_moved__2_2__task_instance as select source.* from task_instance as source left join dag_run as dr on (source.dag_id = dr.dag_id and source.execution_date = dr.execution_date) where dr.id is null ] ``` On further investigation, it looks like `CREATE TABLE AS SELECT ...` queries will cause GTID consistency violations whenever mySQL GTID-based replication is used ([source](https://dev.mysql.com/doc/refman/5.7/en/replication-gtids-restrictions.html)). This is used by default on all Google CloudSQL mySQL instances, so they will always block these queries ([source](https://cloud.google.com/sql/docs/mysql/features#unsupported-statements)) It looks like these queries are created here: https://github.com/apache/airflow/blob/eaa8ac72fc901de163b912a94dbe675045d2a009/airflow/utils/db.py#L739-L747 Could we refactor this into two separate queries like: ```python else: # Postgres, MySQL and SQLite all have the same CREATE TABLE a AS SELECT ... syntax session.execute( text( f"create table {target_table_name} like {source_table}" ) ) session.execute( text( f"INSERT INTO {target_table_name} select source.* from {source_table} as source " + where_clause ) ) ``` In order to preserve the GTID consistency? ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/19988

https://github.com/apache/airflow/pull/19999

480c333c45d31cdfdc63cdfceecd4ad8529eefd4

49965016594618211e47feaaa81f6a03f61ec708

python

closed

apache/airflow

https://github.com/apache/airflow

["BREEZE.rst", "PULL_REQUEST_WORKFLOW.rst", "README.md", "TESTING.rst", "breeze-complete", "docs/apache-airflow/concepts/scheduler.rst", "docs/apache-airflow/howto/set-up-database.rst", "docs/apache-airflow/installation/prerequisites.rst", "scripts/ci/libraries/_initialization.sh"]

Postgres 9.6 end of support

### Describe the issue with documentation since November 11, 2021 the support of Postgres 9.6 is finish https://www.postgresql.org/support/versioning/ ### How to solve the problem remove 9.6 from airflow ### Anything else _No response_ ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/19986

https://github.com/apache/airflow/pull/19987

538612c3326b5fd0be4f4114f85e6f3063b5d49c

a299cbf4ce95af49132a6c7b17cd6a0355544836

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/providers/google/cloud/transfers/gdrive_to_gcs.py", "tests/providers/google/cloud/transfers/test_gdrive_to_gcs.py"]

GoogleDriveToGCSOperator.dry_run() raises AttributeError due to deprecated template_fields

### Apache Airflow Provider(s) google ### Versions of Apache Airflow Providers apache-airflow-providers-google==5.1.0 ### Apache Airflow version 2.1.4 ### Operating System MacOS BigSur 11.6 ### Deployment Virtualenv installation ### Deployment details `tox4` virtual environment ### What happened 1) Unit testing some DAG task operators with `asset op.dry_run() is None` 2) `AttributeError` is raised when `baseoperator` iterates through `template_fields` and calls `getattr` ```python def dry_run(self) -> None: """Performs dry run for the operator - just render template fields.""" self.log.info('Dry run') for field in self.template_fields: content = getattr(self, field) ``` 3) Caused by deprecated `destination_bucket` argument, is being assigned to `self.bucket_name` ```python template_fields = [ "bucket_name", "object_name", "destination_bucket", "destination_object", "folder_id", "file_name", "drive_id", "impersonation_chain", ] def __init__( self, *, bucket_name: Optional[str] = None, object_name: Optional[str] = None, destination_bucket: Optional[str] = None, # deprecated destination_object: Optional[str] = None, # deprecated file_name: str, folder_id: str, drive_id: Optional[str] = None, gcp_conn_id: str = "google_cloud_default", delegate_to: Optional[str] = None, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs, ) -> None: super().__init__(**kwargs) self.bucket_name = destination_bucket or bucket_name if destination_bucket: warnings.warn( "`destination_bucket` is deprecated please use `bucket_name`", DeprecationWarning, stacklevel=2, ) self.object_name = destination_object or object_name if destination_object: warnings.warn( "`destination_object` is deprecated please use `object_name`", DeprecationWarning, stacklevel=2, ) self.folder_id = folder_id self.drive_id = drive_id self.file_name = file_name self.gcp_conn_id = gcp_conn_id self.delegate_to = delegate_to self.impersonation_chain = impersonation_chain ``` ### What you expected to happen Calling `op.dry_run()` should return None and not raise any exceptions. The templated fields contains deprecated arguments (`destination_bucket`, `destination_object`), and aren't initialized in the init method for the class. The base operator loops through these templated fields, but since `GoogleDriveToGCSOperator` does not initialize `self.destination_bucket` or `self.destination_object`, it raises an `AttributeError` ### How to reproduce ```python from airflow.providers.google.cloud.transfers import gdrive_to_gcs # pytest fixtures included as arguments # won't include for brevity, but can provide if necessary def test_gdrive_to_gcs_transfer( test_dag, mock_gcp_default_conn, patched_log_entry, today ): op = gdrive_to_gcs.GoogleDriveToGCSOperator( task_id="test_gcs_to_gdrive_transfer", dag=test_dag, bucket_name="some-other-bucket", object_name="thing_i_want_to_copy.csv", file_name="my_file.csv", folder_id="my_folder", drive_id="some_drive_id", ) assert op.dry_run() is None ``` ### Anything else Not sure where it would be appropriate to address this issue since the deprecated fields support backward compatibility to previous versions of the operator. This is my first time contributing to the project, but hope this is helpful. ### Are you willing to submit PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/19977

https://github.com/apache/airflow/pull/19991

53b241534576f1a85fe7f87ed66793d43f3a564e

cb082d361a61da7040e044ff2c1f7758142a9b2d

python

closed

apache/airflow

https://github.com/apache/airflow

["dev/breeze/src/airflow_breeze/branch_defaults.py", "dev/breeze/src/airflow_breeze/breeze.py", "dev/breeze/src/airflow_breeze/cache.py", "dev/breeze/src/airflow_breeze/ci/build_image.py", "dev/breeze/src/airflow_breeze/ci/build_params.py", "dev/breeze/src/airflow_breeze/console.py", "dev/breeze/src/airflow_breeze/global_constants.py", "dev/breeze/src/airflow_breeze/utils.py"]

Breeze: Build CI images with Breeze

This quite a task and it should likely be split into smaller PRs until completed. Breeze has the possibility of building CI and PROD images, but this one focuses only on building the CI image. Building the image is not very straightforward, because in mulitple places the images are build differently: * for different Python version * with different caches * with different build options (upgrade to newer dependencies for example) Also the image is multiple steps * building base python image (if needed) * building the CI image The whole story about images (both PROD and CI) is described here: https://github.com/apache/airflow/blob/main/IMAGES.rst This change should end up withbuild-ci-image command that should build the image with all the possible flags. NOTE: The old `breeze build image` command also implements complex logic to determine whether images should be pulled or whether they need to be rebuild at all. This is NOT part of this change and it will be implemented later. Here are the current flags. ``` ./breeze build-image --help Good version of docker 20.10.7. Build image Detailed usage for command: build-image breeze build-image [FLAGS] Builds docker image (CI or production) without entering the container. You can pass additional options to this command, such as: Choosing python version: '--python' Choosing cache option: '--build-cache-local' or '-build-cache-pulled', or '--build-cache-none' Choosing whether to force pull images or force build the image: '--force-build-image', '--force-pull-image' Checking if the base python image has been updated: '--check-if-base-python-image-updated' You can also pass '--production-image' flag to build production image rather than CI image. For GitHub repository, the '--github-repository' can be used to choose repository to pull/push images. Flags: -p, --python PYTHON_MAJOR_MINOR_VERSION Python version used for the image. This is always major/minor version. One of: 3.7 3.8 3.9 3.6 -a, --install-airflow-version INSTALL_AIRFLOW_VERSION Uses different version of Airflow when building PROD image. 2.0.2 2.0.1 2.0.0 wheel sdist -t, --install-airflow-reference INSTALL_AIRFLOW_REFERENCE Installs Airflow directly from reference in GitHub when building PROD image. This can be a GitHub branch like main or v2-2-test, or a tag like 2.2.0rc1. --installation-method INSTALLATION_METHOD Method of installing Airflow in PROD image - either from the sources ('.') or from package 'apache-airflow' to install from PyPI. Default in Breeze is to install from sources. One of: . apache-airflow --upgrade-to-newer-dependencies Upgrades PIP packages to latest versions available without looking at the constraints. -I, --production-image Use production image for entering the environment and builds (not for tests). -F, --force-build-images Forces building of the local docker images. The images are rebuilt automatically for the first time or when changes are detected in package-related files, but you can force it using this flag. -P, --force-pull-images Forces pulling of images from GitHub Container Registry before building to populate cache. The images are pulled by default only for the first time you run the environment, later the locally build images are used as cache. --check-if-base-python-image-updated Checks if Python base image from DockerHub has been updated vs the current python base image we store in GitHub Container Registry. Python images are updated regularly with security fixes, this switch will check if a new one has been released and will pull and prepare a new base python based on the latest one. --cleanup-docker-context-files Removes whl and tar.gz files created in docker-context-files before running the command. In case there are some files there it unnecessarily increases the context size and makes the COPY . always invalidated - if you happen to have those files when you build your image. Customization options: -E, --extras EXTRAS Extras to pass to build images The default are different for CI and production images: CI image: devel_ci Production image: amazon,async,celery,cncf.kubernetes,dask,docker,elasticsearch,ftp,google,google_auth, grpc,hashicorp,http,ldap,microsoft.azure,mysql,odbc,pandas,postgres,redis,sendgrid, sftp,slack,ssh,statsd,virtualenv --image-tag TAG Additional tag in the image. --skip-installing-airflow-providers-from-sources By default 'pip install' in Airflow 2.0 installs only the provider packages that are needed by the extras. When you build image during the development (which is default in Breeze) all providers are installed by default from sources. You can disable it by adding this flag but then you have to install providers from wheel packages via --use-packages-from-dist flag. --disable-pypi-when-building Disable installing Airflow from pypi when building. If you use this flag and want to install Airflow, you have to install it from packages placed in 'docker-context-files' and use --install-from-docker-context-files flag. --additional-extras ADDITIONAL_EXTRAS Additional extras to pass to build images The default is no additional extras. --additional-python-deps ADDITIONAL_PYTHON_DEPS Additional python dependencies to use when building the images. --dev-apt-command DEV_APT_COMMAND The basic command executed before dev apt deps are installed. --additional-dev-apt-command ADDITIONAL_DEV_APT_COMMAND Additional command executed before dev apt deps are installed. --additional-dev-apt-deps ADDITIONAL_DEV_APT_DEPS Additional apt dev dependencies to use when building the images. --dev-apt-deps DEV_APT_DEPS The basic apt dev dependencies to use when building the images. --additional-dev-apt-deps ADDITIONAL_DEV_DEPS Additional apt dev dependencies to use when building the images. --additional-dev-apt-envs ADDITIONAL_DEV_APT_ENVS Additional environment variables set when adding dev dependencies. --runtime-apt-command RUNTIME_APT_COMMAND The basic command executed before runtime apt deps are installed. --additional-runtime-apt-command ADDITIONAL_RUNTIME_APT_COMMAND Additional command executed before runtime apt deps are installed. --runtime-apt-deps ADDITIONAL_RUNTIME_APT_DEPS The basic apt runtime dependencies to use when building the images. --additional-runtime-apt-deps ADDITIONAL_RUNTIME_DEPS Additional apt runtime dependencies to use when building the images. --additional-runtime-apt-envs ADDITIONAL_RUNTIME_APT_DEPS Additional environment variables set when adding runtime dependencies. Build options: --disable-mysql-client-installation Disables installation of the mysql client which might be problematic if you are building image in controlled environment. Only valid for production image. --disable-mssql-client-installation Disables installation of the mssql client which might be problematic if you are building image in controlled environment. Only valid for production image. --constraints-location Url to the constraints file. In case of the production image it can also be a path to the constraint file placed in 'docker-context-files' folder, in which case it has to be in the form of '/docker-context-files/<NAME_OF_THE_FILE>' --disable-pip-cache Disables GitHub PIP cache during the build. Useful if GitHub is not reachable during build. --install-from-docker-context-files This flag is used during image building. If it is used additionally to installing Airflow from PyPI, the packages are installed from the .whl and .tar.gz packages placed in the 'docker-context-files' folder. The same flag can be used during entering the image in the CI image - in this case also the .whl and .tar.gz files will be installed automatically -C, --force-clean-images Force build images with cache disabled. This will remove the pulled or build images and start building images from scratch. This might take a long time. -r, --skip-rebuild-check Skips checking image for rebuilds. It will use whatever image is available locally/pulled. -L, --build-cache-local Uses local cache to build images. No pulled images will be used, but results of local builds in the Docker cache are used instead. This will take longer than when the pulled cache is used for the first time, but subsequent '--build-cache-local' builds will be faster as they will use mostly the locally build cache. This is default strategy used by the Production image builds. -U, --build-cache-pulled Uses images pulled from GitHub Container Registry to build images. Those builds are usually faster than when ''--build-cache-local'' with the exception if the registry images are not yet updated. The images are updated after successful merges to main. This is default strategy used by the CI image builds. -X, --build-cache-disabled Disables cache during docker builds. This is useful if you want to make sure you want to rebuild everything from scratch. This strategy is used by default for both Production and CI images for the scheduled (nightly) builds in CI. -g, --github-repository GITHUB_REPOSITORY GitHub repository used to pull, push images. Default: apache/airflow. -v, --verbose Show verbose information about executed docker, kind, kubectl, helm commands. Useful for debugging - when you run breeze with --verbose flags you will be able to see the commands executed under the hood and copy&paste them to your terminal to debug them more easily. Note that you can further increase verbosity and see all the commands executed by breeze by running 'export VERBOSE_COMMANDS="true"' before running breeze. --dry-run-docker Only show docker commands to execute instead of actually executing them. The docker commands are printed in yellow color. ```

https://github.com/apache/airflow/issues/19970

https://github.com/apache/airflow/pull/20338

919ff4567d86a09fb069dcfd84885b496229eea9

95740a87083c703968ce3da45b15113851ef09f7

python

closed

apache/airflow

https://github.com/apache/airflow

[".github/workflows/build-images.yml", ".github/workflows/ci.yml", "dev/breeze/setup.cfg", "dev/breeze/src/airflow_ci/__init__.py", "dev/breeze/src/airflow_ci/freespace.py", "scripts/ci/tools/free_space.sh"]

CI: Rewrite `free_space` script in Python

In our build/ci.yml (maybe other?) files we are using a "free_space" script that performs cleanup of the machine before running the tasks. This allows us to reclaim memory and disk space for our tasks. Example: https://github.com/apache/airflow/blob/eaa8ac72fc901de163b912a94dbe675045d2a009/.github/workflows/ci.yml#L334 This should be written in Python and our ci.yml and build.yml should be updated. We shoudl also be able to remove free_space script from the repo.

https://github.com/apache/airflow/issues/19969

https://github.com/apache/airflow/pull/20200

7222f68d374787f95acc7110a1165bd21e7722a1

27fcd7e0be42dec8d5a68fb591239c4dbb0092f5

python

closed

apache/airflow

https://github.com/apache/airflow

[".pre-commit-config.yaml", "dev/breeze/doc/BREEZE.md"]

Breeze: Make Breeze works for Windows seemleasly

The goal to achieve: We would like to have `./Breeze2.exe` in the main directory of Airlfow which should do the same as `python Breeze2` now - firing the Breeze command line and managing the virtualenv for it. We need to have: * colors in terminal * possibility to use commands and flags * [stretch goal] autocomplete in Windows (we might separate it out to a separate task later)

https://github.com/apache/airflow/issues/19966

https://github.com/apache/airflow/pull/20148

97261c642cbf07db91d252cf6b0b7ff184cd64c6

9db894a88e04a71712727ef36250a29b2e34f4fe

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/dagrun.py"]

Airflow crashes with a psycopg2.errors.DeadlockDetected exception

### Apache Airflow version 2.2.2 (latest released) ### Operating System Ubuntu 21.04 on a VM ### Versions of Apache Airflow Providers root@AI-Research:~/learning_sets/airflow# pip freeze | grep apache-airflow-providers apache-airflow-providers-ftp==2.0.1 apache-airflow-providers-http==2.0.1 apache-airflow-providers-imap==2.0.1 apache-airflow-providers-sqlite==2.0.1 ### Deployment Other ### Deployment details Airflow is at version 2.2.2 psql (PostgreSQL) 13.5 (Ubuntu 13.5-0ubuntu0.21.04.1) The dag contains thousands of tasks for data download and preprocessing and preparation which is destined to a mongodb database (so, I'm not using the PostgreSQL inside my tasks). ### What happened [2021-12-01 19:41:57,556] {scheduler_job.py:644} ERROR - Exception when executing SchedulerJob._run_scheduler_loop Traceback (most recent call last): File "/usr/local/lib/python3.9/dist-packages/sqlalchemy/engine/base.py", line 1276, in _execute_context self.dialect.do_execute( File "/usr/local/lib/python3.9/dist-packages/sqlalchemy/engine/default.py", line 608, in do_execute cursor.execute(statement, parameters) psycopg2.errors.DeadlockDetected: deadlock detected DETAIL: Process 322086 waits for ShareLock on transaction 2391367; blocked by process 340345. Process 340345 waits for AccessExclusiveLock on tuple (0,26) of relation 19255 of database 19096; blocked by process 340300. Process 340300 waits for ShareLock on transaction 2391361; blocked by process 322086. HINT: See server log for query details. CONTEXT: while updating tuple (1335,10) in relation "task_instance" The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/usr/local/lib/python3.9/dist-packages/airflow/jobs/scheduler_job.py", line 628, in _execute self._run_scheduler_loop() File "/usr/local/lib/python3.9/dist-packages/airflow/jobs/scheduler_job.py", line 709, in _run_scheduler_loop num_queued_tis = self._do_scheduling(session) File "/usr/local/lib/python3.9/dist-packages/airflow/jobs/scheduler_job.py", line 792, in _do_scheduling callback_to_run = self._schedule_dag_run(dag_run, session) File "/usr/local/lib/python3.9/dist-packages/airflow/jobs/scheduler_job.py", line 1049, in _schedule_dag_run dag_run.schedule_tis(schedulable_tis, session) File "/usr/local/lib/python3.9/dist-packages/airflow/utils/session.py", line 67, in wrapper return func(*args, **kwargs) File "/usr/local/lib/python3.9/dist-packages/airflow/models/dagrun.py", line 898, in schedule_tis session.query(TI) File "/usr/local/lib/python3.9/dist-packages/sqlalchemy/orm/query.py", line 4063, in update update_op.exec_() File "/usr/local/lib/python3.9/dist-packages/sqlalchemy/orm/persistence.py", line 1697, in exec_ self._do_exec() File "/usr/local/lib/python3.9/dist-packages/sqlalchemy/orm/persistence.py", line 1895, in _do_exec self._execute_stmt(update_stmt) File "/usr/local/lib/python3.9/dist-packages/sqlalchemy/orm/persistence.py", line 1702, in _execute_stmt self.result = self.query._execute_crud(stmt, self.mapper) File "/usr/local/lib/python3.9/dist-packages/sqlalchemy/orm/query.py", line 3568, in _execute_crud return conn.execute(stmt, self._params) File "/usr/local/lib/python3.9/dist-packages/sqlalchemy/engine/base.py", line 1011, in execute return meth(self, multiparams, params) File "/usr/local/lib/python3.9/dist-packages/sqlalchemy/sql/elements.py", line 298, in _execute_on_connection return connection._execute_clauseelement(self, multiparams, params) File "/usr/local/lib/python3.9/dist-packages/sqlalchemy/engine/base.py", line 1124, in _execute_clauseelement ret = self._execute_context( File "/usr/local/lib/python3.9/dist-packages/sqlalchemy/engine/base.py", line 1316, in _execute_context self._handle_dbapi_exception( File "/usr/local/lib/python3.9/dist-packages/sqlalchemy/engine/base.py", line 1510, in _handle_dbapi_exception util.raise_( File "/usr/local/lib/python3.9/dist-packages/sqlalchemy/util/compat.py", line 182, in raise_ raise exception File "/usr/local/lib/python3.9/dist-packages/sqlalchemy/engine/base.py", line 1276, in _execute_context self.dialect.do_execute( File "/usr/local/lib/python3.9/dist-packages/sqlalchemy/engine/default.py", line 608, in do_execute cursor.execute(statement, parameters) sqlalchemy.exc.OperationalError: (psycopg2.errors.DeadlockDetected) deadlock detected DETAIL: Process 322086 waits for ShareLock on transaction 2391367; blocked by process 340345. Process 340345 waits for AccessExclusiveLock on tuple (0,26) of relation 19255 of database 19096; blocked by process 340300. Process 340300 waits for ShareLock on transaction 2391361; blocked by process 322086. HINT: See server log for query details. CONTEXT: while updating tuple (1335,10) in relation "task_instance" [SQL: UPDATE task_instance SET state=%(state)s WHERE task_instance.dag_id = %(dag_id_1)s AND task_instance.run_id = %(run_id_1)s AND task_instance.task_id IN (%(task_id_1)s, %(task_id_2)s, %(task_id_3)s, %(task_id_4)s, %(task_id_5)s, %(task_id_6)s, %(task_id_7)s, %(task_id_8)s, %(task_id_9)s, %(task_id_10)s, %(task_id_11)s, %(task_id_12)s, %(task_id_13)s, %(task_id_14)s, %(task_id_15)s, %(task_id_16)s, %(task_id_17)s, %(task_id_18)s, %(task_id_19)s, %(task_id_20)s)] [parameters: {'state': <TaskInstanceState.SCHEDULED: 'scheduled'>, 'dag_id_1': 'download_and_preprocess_sets', 'run_id_1': 'manual__2021-12-01T17:31:23.684597+00:00', 'task_id_1': 'download_1379', 'task_id_2': 'download_1438', 'task_id_3': 'download_1363', 'task_id_4': 'download_1368', 'task_id_5': 'download_138', 'task_id_6': 'download_1432', 'task_id_7': 'download_1435', 'task_id_8': 'download_1437', 'task_id_9': 'download_1439', 'task_id_10': 'download_1457', 'task_id_11': 'download_168', 'task_id_12': 'download_203', 'task_id_13': 'download_782', 'task_id_14': 'download_1430', 'task_id_15': 'download_1431', 'task_id_16': 'download_1436', 'task_id_17': 'download_167', 'task_id_18': 'download_174', 'task_id_19': 'download_205', 'task_id_20': 'download_1434'}] (Background on this error at: http://sqlalche.me/e/13/e3q8) [2021-12-01 19:41:57,566] {local_executor.py:388} INFO - Shutting down LocalExecutor; waiting for running tasks to finish. Signal again if you don't want to wait. [2021-12-01 19:42:18,013] {process_utils.py:100} INFO - Sending Signals.SIGTERM to GPID 285470 [2021-12-01 19:42:18,105] {process_utils.py:66} INFO - Process psutil.Process(pid=285470, status='terminated', exitcode=0, started='18:56:21') (285470) terminated with exit code 0 [2021-12-01 19:42:18,106] {scheduler_job.py:655} INFO - Exited execute loop ### What you expected to happen Maybe 24 concurrent processes/tasks are too many? ### How to reproduce reproducibility is challenging, but maybe the exception provides enough info for a fix ### Anything else all the time, after some time the dag is being run ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/19957

https://github.com/apache/airflow/pull/20894

14a057ff921d7e6ceb70326f5fecac29d3a093ad

0e4a057cd8f704a51376d9694c0114eb2ced64ef

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/www/views.py"]

Add hour and minute to time format on x-axis in Landing Times

### Description It would be great if the date and time format of x-axis in Landing Times is `%d %b %Y, %H:%M` instead of `%d %b %Y` to see `execution_date` of task instances. ### Use case/motivation For x-axis of all line chart using `nvd3.lineChart`, the date and time format has only date without time because its format is `%d %b %Y` by default when `x_is_date` is `True`. From what I have seen in version 1.7, the date and time format was like `%d %b %Y, %H:%M`. It has been changed as Highcharts was no longer used since version 1.8. It is simple to fix by injecting `x_axis_format="%d %b %Y, %H:%M"` where every `nvd3.lineChart` class is initialized. ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/19955

https://github.com/apache/airflow/pull/20002

2c80aaab4f486688fa4b8e252e1147a5dfabee54

6a77e849be3a505bf2636e8224862d77a4719621

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/migrations/versions/54bebd308c5f_add_trigger_table_and_task_info.py", "airflow/migrations/versions/7b2661a43ba3_taskinstance_keyed_to_dagrun.py", "airflow/migrations/versions/e9304a3141f0_make_xcom_pkey_columns_non_nullable.py"]

Running alembic migration downgrade on database causes error

### Apache Airflow version 2.2.2 (latest released) ### Operating System PRETTY_NAME="Debian GNU/Linux 10 (buster)" NAME="Debian GNU/Linux" VERSION_ID="10" VERSION="10 (buster)" VERSION_CODENAME=buster ID=debian HOME_URL="https://www.debian.org/" SUPPORT_URL="https://www.debian.org/support" BUG_REPORT_URL="https://bugs.debian.org/" ### Versions of Apache Airflow Providers apache-airflow-providers-amazon==2.4.0 apache-airflow-providers-celery==2.1.0 apache-airflow-providers-cncf-kubernetes==2.1.0 apache-airflow-providers-datadog==2.0.1 apache-airflow-providers-docker==2.3.0 apache-airflow-providers-elasticsearch==2.1.0 apache-airflow-providers-ftp==2.0.1 apache-airflow-providers-google==6.1.0 apache-airflow-providers-grpc==2.0.1 apache-airflow-providers-hashicorp==2.1.1 apache-airflow-providers-http==2.0.1 apache-airflow-providers-imap==2.0.1 apache-airflow-providers-microsoft-azure==3.3.0 apache-airflow-providers-mysql==2.1.1 apache-airflow-providers-odbc==2.0.1 apache-airflow-providers-postgres==2.3.0 apache-airflow-providers-redis==2.0.1 apache-airflow-providers-sendgrid==2.0.1 apache-airflow-providers-sftp==2.2.0 apache-airflow-providers-slack==4.1.0 apache-airflow-providers-sqlite==2.0.1 apache-airflow-providers-ssh==2.3.0 ### Deployment Docker-Compose ### Deployment details _No response_ ### What happened My current database alembic_version is set at e9304a3141f0 While running the command `alembic -c alembic.ini downgrade a13f7613ad25` I received the following error: INFO [alembic.runtime.migration] Running downgrade e9304a3141f0 -> 83f031fd9f1c, make xcom pkey columns non-nullable Traceback (most recent call last): File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1277, in _execute_context cursor, statement, parameters, context File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/engine/default.py", line 608, in do_execute cursor.execute(statement, parameters) psycopg2.errors.InvalidTableDefinition: column "key" is in a primary key The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/airflow/.local/bin/alembic", line 8, in <module> sys.exit(main()) File "/home/airflow/.local/lib/python3.7/site-packages/alembic/config.py", line 588, in main CommandLine(prog=prog).main(argv=argv) File "/home/airflow/.local/lib/python3.7/site-packages/alembic/config.py", line 582, in main self.run_cmd(cfg, options) File "/home/airflow/.local/lib/python3.7/site-packages/alembic/config.py", line 562, in run_cmd **dict((k, getattr(options, k, None)) for k in kwarg) File "/home/airflow/.local/lib/python3.7/site-packages/alembic/command.py", line 366, in downgrade script.run_env() File "/home/airflow/.local/lib/python3.7/site-packages/alembic/script/base.py", line 563, in run_env util.load_python_file(self.dir, "env.py") File "/home/airflow/.local/lib/python3.7/site-packages/alembic/util/pyfiles.py", line 92, in load_python_file module = load_module_py(module_id, path) File "/home/airflow/.local/lib/python3.7/site-packages/alembic/util/pyfiles.py", line 108, in load_module_py spec.loader.exec_module(module) # type: ignore File "<frozen importlib._bootstrap_external>", line 728, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "migrations/env.py", line 107, in <module> run_migrations_online() File "migrations/env.py", line 101, in run_migrations_online context.run_migrations() File "<string>", line 8, in run_migrations File "/home/airflow/.local/lib/python3.7/site-packages/alembic/runtime/environment.py", line 851, in run_migrations self.get_context().run_migrations(**kw) File "/home/airflow/.local/lib/python3.7/site-packages/alembic/runtime/migration.py", line 620, in run_migrations step.migration_fn(**kw) File "/home/airflow/.local/lib/python3.7/site-packages/airflow/migrations/versions/e9304a3141f0_make_xcom_pkey_columns_non_nullable.py", line 76, in downgrade bop.alter_column("execution_date", type_=_get_timestamp(conn), nullable=True) File "/usr/local/lib/python3.7/contextlib.py", line 119, in __exit__ next(self.gen) File "/home/airflow/.local/lib/python3.7/site-packages/alembic/operations/base.py", line 374, in batch_alter_table impl.flush() File "/home/airflow/.local/lib/python3.7/site-packages/alembic/operations/batch.py", line 107, in flush fn(*arg, **kw) File "/home/airflow/.local/lib/python3.7/site-packages/alembic/ddl/postgresql.py", line 185, in alter_column **kw File "/home/airflow/.local/lib/python3.7/site-packages/alembic/ddl/impl.py", line 240, in alter_column existing_comment=existing_comment, File "/home/airflow/.local/lib/python3.7/site-packages/alembic/ddl/impl.py", line 197, in _exec return conn.execute(construct, multiparams) File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1011, in execute return meth(self, multiparams, params) File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/sql/ddl.py", line 72, in _execute_on_connection return connection._execute_ddl(self, multiparams, params) File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1073, in _execute_ddl compiled, File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1317, in _execute_context e, statement, parameters, cursor, context File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1511, in _handle_dbapi_exception sqlalchemy_exception, with_traceback=exc_info[2], from_=e File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/util/compat.py", line 182, in raise_ raise exception File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1277, in _execute_context cursor, statement, parameters, context File "/home/airflow/.local/lib/python3.7/site-packages/sqlalchemy/engine/default.py", line 608, in do_execute cursor.execute(statement, parameters) sqlalchemy.exc.ProgrammingError: (psycopg2.errors.InvalidTableDefinition) column "key" is in a primary key [SQL: ALTER TABLE xcom ALTER COLUMN key DROP NOT NULL] It seems the DOWN migration step for revision `e9304a3141f0 - Make XCom primary key columns non-nullable` is not backwards compatible as it is attempting to make a Primary Key nullable, which is not possible. I suspect the revision of `bbf4a7ad0465 - Remove id column from xcom` has something to do with this - id perhaps being the old primary key for the table. Revision list: https://airflow.apache.org/docs/apache-airflow/stable/migrations-ref.html ### What you expected to happen I expected the alembic version to downgrade to the appropriate version ### How to reproduce On airflow image 2.2.2 attempt to run `alembic -c alembic.ini downgrade {any version lower than revision id e9304a3141f0}` ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/19945

https://github.com/apache/airflow/pull/19994

0afed43a8afde093277be2862138cb32fba8ed29

14bff0e044a6710bc1d8f7b4dc2c9a40754de4a7

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/operators/python.py", "tests/operators/test_python.py"]

ShortCircuitOperator does not save to xcom

### Description `ShortCircuitOperator` does not return any value regardless of the result. Still, if `condition` evaluates to `falsey`, it can be useful to store/save the result of the condition to XCom so that downstream tasks. can use. Many objects evaluates to True/False, not just booleans - see https://docs.python.org/3/library/stdtypes.html ### Use case/motivation The change is trivial and would allow to combine a Python task and ShortCircuit into one.: 1. if callable returns None (or False) -> skip 2. if callable returns non-empty object (or True) -> continue. The proposed change is to pass on the condition to XCom. ### Related issues _No response_ ### Are you willing to submit a PR? - [X] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/19922

https://github.com/apache/airflow/pull/20071

993ed933e95970d14e0b0b5659ad28f15a0e5fde

4f964501e5a6d5685c9fa78a6272671a79b36dd1

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/models/dag.py", "airflow/models/dagrun.py", "tests/models/test_dag.py", "tests/models/test_dagrun.py", "tests/models/test_taskinstance.py"]

Task-level end_date stops entire DAG

### Apache Airflow version 2.2.2 (latest released) ### Operating System Ubuntu 19.10 ### Versions of Apache Airflow Providers _No response_ ### Deployment Other Docker-based deployment ### Deployment details _No response_ ### What happened If a task has an `end_date`, the whole DAG stops at that time instead of only that task. ### What you expected to happen The task with the `end_date` should stop at that time and the rest should continue. ### How to reproduce ``` from datetime import datetime, timedelta from airflow.models import DAG from airflow.operators.dummy import DummyOperator default_args = { "owner": "me", "email": [""], "start_date": datetime(2021, 11, 20), } dag = DAG( "end_date_test", default_args=default_args, schedule_interval="@daily", ) task1 = DummyOperator( task_id="no_end_date", dag=dag ) task2 = DummyOperator( task_id="special_start_end", start_date=datetime(2021, 11, 22), end_date=datetime(2021, 11, 24), dag=dag, ) ```  ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/19917

https://github.com/apache/airflow/pull/20920

f612a2f56add4751e959625c49368d09a2a47d55

85871eba420f3324432f55f74fe57005ff47a21c

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/utils/trigger_rule.py", "airflow/utils/weight_rule.py", "tests/utils/test_trigger_rule.py", "tests/utils/test_weight_rule.py"]

Refactor `TriggerRule` & `WeightRule` classes to inherit from Enum

### Body Both `TriggerRule` & `WeightRule` classes should inherit from Enum https://github.com/apache/airflow/blob/3172be041e3de95f546123e3b66f06c6adcf32d5/airflow/utils/weight_rule.py#L22 https://github.com/apache/airflow/blob/8fde9a4b5b1360f733d86a04c2b0b747f0bfbf01/airflow/utils/trigger_rule.py#L22 An initial attempt to handle this was in https://github.com/apache/airflow/pull/5302 This is something that was also raised during review in https://github.com/apache/airflow/pull/18627#discussion_r719304267 ### Committer - [X] I acknowledge that I am a maintainer/committer of the Apache Airflow project.

https://github.com/apache/airflow/issues/19905

https://github.com/apache/airflow/pull/21264

d8ae7df08168fd3ab92ed0d917f9b5dd34d1354d

9ad4de835cbbd296b9dbd1ff0ea88c1cd0050263

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/decorators/task_group.py", "tests/utils/test_task_group.py"]

Error setting dependencies on task_group defined using the decorator

### Apache Airflow version 2.2.2 (latest released) ### Operating System MacOS 11.6.1 ### Versions of Apache Airflow Providers $ pip freeze | grep airflow apache-airflow==2.2.2 apache-airflow-providers-celery==2.1.0 apache-airflow-providers-ftp==2.0.1 apache-airflow-providers-http==2.0.1 apache-airflow-providers-imap==2.0.1 apache-airflow-providers-sqlite==2.0.1 ### Deployment Other ### Deployment details `airflow standalone` ### What happened ``` AttributeError: 'NoneType' object has no attribute 'update_relative' ``` ### What you expected to happen Task group should be set as downstream of `start` task, and upstream of `end` task ### How to reproduce * Add the following code to dags folder ```python from datetime import datetime from airflow.decorators import dag, task, task_group @dag(start_date=datetime(2023, 1, 1), schedule_interval="@once") def test_dag_1(): @task def start(): pass @task def do_thing(x): print(x) @task_group def do_all_things(): do_thing(1) do_thing(2) @task def end(): pass start() >> do_all_things() >> end() test_dag_1() ``` * Run `airflow standalone` ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/19903

https://github.com/apache/airflow/pull/20671

f2039b4c9e15b514661d4facbd710791fe0a2ef4

384fa4a87dfaa79a89ad8e18ac1980e07badec4b

python

closed

apache/airflow

https://github.com/apache/airflow

["airflow/decorators/base.py", "tests/utils/test_task_group.py"]

DuplicateTaskIdFound when reusing tasks with task_group decorator

### Apache Airflow version 2.2.2 (latest released) ### Operating System MacOS 11.6.1 ### Versions of Apache Airflow Providers $ pip freeze | grep airflow apache-airflow==2.2.2 apache-airflow-providers-celery==2.1.0 apache-airflow-providers-ftp==2.0.1 apache-airflow-providers-http==2.0.1 apache-airflow-providers-imap==2.0.1 apache-airflow-providers-sqlite==2.0.1 ### Deployment Other ### Deployment details `airflow standalone` ### What happened Exception raised : ``` raise DuplicateTaskIdFound(f"Task id '{key}' has already been added to the DAG") airflow.exceptions.DuplicateTaskIdFound: Task id 'do_all_things.do_thing__1' has already been added to the DAG ``` ### What you expected to happen _No response_ ### How to reproduce * Add the following python file to the `dags` folder: ```python from datetime import datetime from airflow.decorators import dag, task, task_group @dag(start_date=datetime(2023, 1, 1), schedule_interval="@once") def test_dag_1(): @task def start(): pass @task def do_thing(x): print(x) @task_group def do_all_things(): for i in range(5): do_thing(i) @task def end(): pass start() >> do_all_things() >> end() test_dag_1() ``` * Start airflow by running `airflow standalone` ### Anything else _No response_ ### Are you willing to submit PR? - [ ] Yes I am willing to submit a PR! ### Code of Conduct - [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

https://github.com/apache/airflow/issues/19902

https://github.com/apache/airflow/pull/20870

077bacd9e7cc066365d3f201be2a5d9b108350fb

f881e1887c9126408098919ecad61f94e7a9661c

python